Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders
Authors: Qiming Hu
Linlong Fan
luoyiyan
Yuhang Yu
Xiaojie Guo
Qingnan Fan
Venue: NeurIPS 2025 poster ()
Keywords: Image Super-Resolution
Text Structure Preservation
Text Segmentation
Multi-task Learning
Diffusion Model
Primary area: applications
TL;DR: We propose TADiSR, a text-aware diffusion model with joint image super-resolution and segmentation decoders, achieving accurate full-image text SR under real-world degradations.

Abstract: The introduction of generative models has significantly advanced image super-resolution (SR) in handling real-world degradations. However, they often incur fidelity-related issues, particularly distorting textual structures. 
In this paper, we introduce a novel diffusion-based SR framework, namely TADiSR, which integrates text-aware attention and joint segmentation decoders to recover not only natural details but also the structural fidelity of text regions in degraded real-world images. Moreover, we propose a complete pipeline for synthesizing high-quality images with fine-grained full-image text masks, combining realistic foreground text regions with detailed background content. Extensive experiments demonstrate that our approach substantially enhances text legibility in super-resolved images, achieving state-of-the-art performance across multiple evaluation metrics and exhibiting strong generalization to real-world scenarios. Our code is available at [here](https://github.com/mingcv/TADiSR).

Text-Aware Real- Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders
Qiming Hule
Linlong Fan Yiyan Luo', Yuhang Yu', Xiaojie Guo Qingnan Fan
College of Intelligence and Computing, Tianjin University
vvvv Mobile Communication Co. Ltd
huqimingStju. edu yuyuhangavivo.cm com xj max gwwgguuullmma.o com fguchina@gmail.com
edu. en fanl famlinlon7003@166 com 1uoxiaohee13330mma1..cm com
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Figure 1: Left: visual comparison between SR results on woald samples. Our method shows clear advantage in restoring text structures over previous GAN-based 322 4. diffusion-basee [35] 34
and text image SR [12 46 methods. Right: cross-attention to the word "text" before and after LoRA fine-tuning with joint segmentation decoders, showing improved ability perceive text regions.
Abstract
The introduction of generative models has significantly advanced image super- resolution (SR) in handling real-world degradations. However, they often incur fidelity-related issues, particularly distorting textual structures. this paper, we introduce novel diffusion-based SR framework, namely TADiSR, which integrates text-aware attention and joint segmentation decoders to recover not only natural details but also the structural fidelity of text regions in degraded real-world images. Moreover, we propose a complete pipeline for synthesizing high-qualit, images with fine- mne-graieed full-image masks, combining realistic foreground text regions with detailed background content. Extensive experiments demonstrate that our approach substantially enhances text legibility super-resolved images, achieving state-of stat-oof-hhe-at the-art performance across multiple evaluation metrics and exhibiting strong generalization to real-world scenarios. Our code is available at here
Introduction
Real-world image super-resolution (Real-SR) is challenging task that aims to recover high-resolution (HR) images from low-resolution (LR) inputs affected by unknown and coupled degradation factors
"Equal contribution.
Corresponding authors.
This work was completed during an internship at vivo.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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encountered in real scenarios. In recent years, generative models such as Generative Adversarial Networks (GANs) 32
16 34 have been introduced into Real-SR, leveraging generative priors to hallucinate missing details lost due to degradation. Generative models can produce visually realistic images, but often sacrifice structural accuracy for perceptual quality, raising concerns about the trade-off between fidelity and realism [17]. One of the most prominent issues arises in textual content, particularly in languages with complex stroke structures such as Chinese [1]. As illustrated on the left side of Fig. [ previous generative models often struggle to perceive and preserve textual structure in reconstructed images, resulting in severe distortions such as malformed strokes incorrect characters. These issues not only degrade user experience but also hinder downstream applications that depend on accurate text restoration.
14 21 and Diffusion Models 42
29
39
35
To accurately restore text structures degraded in real-world images, we propose to fine-tune the cross-attention mechanism between text and image tokens a pre-trained diffusion model. As shown in Fig. F (right), we visualize the cross-attention response to the word "text" using DAAM 25 and observe that the original model fails to attend properly to text regions. However, by introducing a LoRA-based fine-tuning strategy [TI] with joint image super-resolution and text segmentation task, the model learns to focus its cross-attention on textual areas. These cross-attention maps, after a linear projection, are fed into a dedicated text segmentation decoder to produce high-quality text masks. This observation leads to two key insights: (1) LoRA fine-tuning can effectively guide the cross-attention of diffusion models toward previously under-attended semantic categories; and (2) text-aware reper-resolution and text segmentation are highly complementary and can be unified in a multi-task learning framework. Based on these insights, propose TADiSR (Text-Aware Diffusion model for real-world image Super-RRsoluttonn which replaces the standard VAE image decoder with pair of image and text segmentation decoders. The decoders take as input the cross-attention response together with the denoised latents, conducting mmage-segmennation interactions through multi-scale, dual-stream manner. TADiSR can significantly enhance the fidelity of text regions in super-resolved images while preserving general image quality. Unlike prior text SR methods that require multi-stage process of detection, regional SR, and fusion, our approach is end-to-ene producing the final output in a single pass for better practicality.
19
46
Based the above amalysis, dataset that contains both accurate text segmentation masks and high- quality background scenes is essential for advancing text-aware image super-reson resouurcso However, no existing dataset fully satisfies these requirements. Existing text segmentation datasets, such as TextSeg (37] and BTS 38) mainly contain cropped text regions with limited background detail and possible degradation, and most restricted to English, except for the bilingual BTS. General-purpose super-resolution datasets such as DIV2K M. Flicker2K (27]. and LSDIR [33 offer rich image details but contain limited text content and lack any form of text region annotation. Scene text image super-resolution datasets like Real-CE [8 typically provide both low-resolution and high-resolution image pairs in text-rich scenarios. but they are often small in scale and do not include ground-trul -ruuh text segmentation masks. To address this gap, we introduce novel data synthesis pipeline that constructs a full-image text image super-resolution dataset. We first apply text segmentation model, fine-tuned on bilingual dataset, to large-scale text recognition datasets to extract a diverse set of segmented text patches. These patches are filtered using an OCR model to ensure segmentation accuracy. We then apply a super-resolution model to restore the original quality of these patches and paste them randomly onto high-quality background images drawn from existing general-purposs super-resolution datasets. This process yields our Full-image Text image Super- Resolutino (FTSR) dataset, which contains accurate foreground text masks and rich background details.
Our main contributions are summarized as follows:
We propose TADiSR, a text-aware diffusion- super-resolution framework that jointly performs image super-resolution and text segmentation. By incorporating a text-aware cross-attention fine-tuning mechanism and joint segmentation decoders, TADiSR effectively enhances text perception and structural fidelity in real-world degraded scenes.
We propose a scalable data synthesis pipeline for text-aware image super-resolution, enabling the construction of the FTSR dataset with accurate text masks and backgrounds with rich details. The dataset is easily extendable with additional text-oriented images.
Extensive experiments demonstrate that our approach significantly outperforms prior state- of-the-art models both qualitative and quantitative evaluations in both synthetic and real-world scenarios, especially in preserving text structure fidelity.
2

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2 Related Work
Real-World Image Super-Resolution. Real-world image super-resolution (Real-SR) builds upon blind SR by modeling complex, composite degradations that occur in uncontrolled environments. Early methods such as BSRGAN 44 and Real -SR-EEN [32 simulate diverse degradation processes through randomized application orders multi- stage pipelines, and employ adversarial training to generate visually realistic outputs. Despite their success in producing natural-looking images, GAN-based approaches often fail accurately recover fine details, especially structural elements like textures and text, due to their limited ability to capture high-level semantics and unstable training dynamics. Recent advances in generative modeling have introduced diffusion based methods to Real-SR 33 42, I6], offering improved stability and generative fidelity, yet challenges remain in accurately restoring complex structures, particularly in text-heavy scenarios. ResShift 42 accelerates the denoising sampling of LDM [23 by progressively shifting residuals between LR and HR images during forward propagation. StableSR [29] introduces time-aware encoders, controllable feature wrapping, and novel sampling strategies to fine-tune pre-trained diffusion models, circumventing expensive training costs. SinSR (33] proposes deterministic sampling and munsistency-prserviin dis- tillation to compress ResShift's 's sampling into a single-step execution. DifBIR [16] decomposes blind restoration into degradation removal and detail enhancement stages with adaptive
balance based on regional detail richness. PASI (39] employs ControlNet with degradation-cleaned pixel-domain inputs to enhance pixel-Ievel fidelity. SeeSR [35] develops a degradation robustt tag model generating semantic prompts to improve semantic fidelity in real- SR. SupIR [41] intro- duces large-scale high-quality image-text pairs and degradation-robust encoders for latent alignment, complemented by Trimmed ControlNet for efficient restoration control. Wu et al. [34] proposed OSEDiff, which directly takes low-resolution images as the starting point for diffusion, and the variational score distillation is applied in the latent space to ensure one-step sampling. Despite notable improvements in realism and fidelity for general content, these methods often overlook or distort text, due insufficient sensitivity to character-level structures.
Text Image Super-Resolution Text image super-resolution (Text-SR) aims to enhance the legibility of textual content, often by processing cropped image patches containing isolated words or lines. Early approaches, such as those by Dong et al. [6], applied general SR techniques like SRCNN (5) to improve OCR performance on low-resolution inputs. TexISR B1 integrates text recognizers into
GAN architectures where text recognition loss is backpropagated to guide the generation of legible characters. PlugNet [20] embedded SR units into text recognition training, sharing the backbone to enable more discriminative representations under degraded conditions. TSRN (30] incorporated edge- aware modules and introduced the TexiZoom dataset better simulate real-world text degradation via varying camera focal lengths. Transformer- leveraged global attention and dedicated text recognition head to sharpen textual features through position and content-aware losses. TATT 19 proposed a global attention module within CNNs to better handle irregular text layouts. More recent efforts have leveraged powerful generative priors to advance Text-SR: MARCONet [12) combines Transformer backbones with codebooks and StyleGAN priors recover diverse character styles, while DiffTSR 46) introduced dual-stream diffusion model that denoises pure text and text image components, respectively, interacting features via a -ooturr-o--Modlllii
methods have further advanced the field. STT (2)
mechanism. Although these methods achieve oftte--f-thh-aar performance on cropped text regions, they struggle to generalize to full images, especially when faced with multi-line, vertical, long, and complex layout text. These methods typically require additional models and complex processing steps to achieve text-aware full-image super-resolution. This highlights the need for a unified and practical solution for full-image text image super-resolution. Furthermore, these works demonstrate the mutual benefits between text image super-resolution and text recognition tasks, suggesting the potential of multi-task learning frameworks. By extending this insight to the full-image solution, our approach integrates text-aware super-resolution and text segmentation into unified diffusion--ased architecture that directly processes whole images.
3 Methodology
3.1 Overall Architecture
The overall architecture of the proposed TADISR model illustrated in Fig. 2 Built upon the Latent Diffusion Model (LDM) framework (23) TADiSR introduces two key components, Awat-Aware
3

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Image Decoder
CrossDeeoder Interaction Block
TADiSR
Ixl,
Toxt Enendor
SiLU
"best quality, high
GroupNorn
GroupNorm
resolution,
LoRA
Prompts y
Adapters
N
SR Output
Ixl, coDv
Ixl, conv
WIE MIEE
SRIFEE LALKO
ResBBlok
ResBllck
VAE
ResBlock
ResBlock
Image Branch
Text Beanch
Input Xg
Text Seg- mentation 3
Cross-Decoder Interactions
Text-Aware Cross-Ateetii Atteation
Text Segmen-
responses "text"
Linear
tation Decoder
Hadamard product
Figure 2: Overall architecture of the proposed TADiSR model. Text-aware to the word "text" are extracted from the denoising U-Net of the LDM. A Text Segmentation Decoder introduced, which takes the linearly projected pross-attention response as input and is jointly trained with the original VAE decoder via Joint Segmentation Decoders (JSD). This design enables simultaneous generation super-resolved images and text segmentation maps. LoRA adapters applied to fine-tune the U-Net (including cross-attention layers) and the VAE decoder. The detailed structure of CDIB depicted on the right.
responses
Interaction Blocks (CDIB), forming the
Cross-Attention (TACA) and Joint Segmentation Decoders (JSD), to equip the diffusion model with fine-gri fine-grained text perception capabilities.
Given a degraded low-resolution image x,, and the fixed prompt y, the LR image encoded by the VAE encoder Eg into latent representation by the text encoder Po to obtain the contextual embedding cy = Po(y). We denote the embedding slice corresponding to the keyword "text" as Cex Here, 0 represents the parameters of the pre- preitrained backbone, and & denotes the LoRA fine-tuned parameters. During the diffusion process, the latent representation 2 is denoised by the U-Net backbone to produce the latent 2 by one step:
Eg (x), while the prompt is processed
2H (zz B, with ñ U
(1)
where and Be are scalars determined by the predefined diffusion time step t. The noise estimation
is predicted by the denoising U-Net Ug. In parallel, the cooss-attettion maps that respond to Cex are linearly projected and fed, along with 2 into the Joint Segmentation Decoders. The decoders yield two predictions: a high-quality super-resolved image & and text segmentation mask s. The following two subsections detail the design of the text-aware cross-attention mechanism and the structure of our joint segmentation decoders.
3.2 Text-Aware Cross-Attention
In LDM, the cross-attention mechanism [28 is used within the intermediate layers of the U-Net to inject textual conditions into the visual feature stream. is defined as:
x TT xv, with q = Wg x x2, k W Cy. v W x Cg. (2)
CA(q. k, v) = softmax
Vd
where Wq. Wk. and Wy are learnable linear projection matrices, x denotes matrix multiplication, represents intermediate image latent the U-Net. q. k, and are the query, key, and value matrices, respectively, with dimensionality of d.
We enhance the textual content awareness of LDM by guiding the attention responses corresponding to the keyword "text" in the prompt to the text regions. Since the prompt is free-form, the position of the keyword "text" is not fixed. Therefore,
first identify the indices of its token slice Ciex within the full conditioning vector cy. These indices are then used to select the corresponding slice from each cross-attention map a qm x (km))T The collection of these slices across all M layers is:
L...M [a
a

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where m E {1...M} } is the index of a cross -ross-tttentionn layer, and a is the attention slice corresponding to the indices of Ciex. These responses are concatenated along the token channel via Concat() and passed through linear projection to match the latent dimension of the denoised image code 2n::
tex
Wa x Concat (a
(4)
where Wa is the learnable projection matrix. The resulting atex is subsequently fed, together with 2H into the Joint Segmentation Decoders for further joint text segmentation and image super-resolution.
3.3 Joint Segmentation Decoders
To enable multi-task learning for both text-aware image super-resolution and text segmentation, introduce Joint Segmentation Decoders. Specifically, in addition to the original VAE image decoder D% in LDM, we design a symmetrie Text Segmentation Decoder D. These two decoders jointly decode the denoised image latent 2и and the aggregated text-aware cross-attention a obtaining , (super-resolved image) and & (text segmentation mask) by:
H
(2H),
$ = D (atex)
(5)
where & denotes LoRA fine-tuned parameters and ip are randomly initialized. Interaction between the two decoders facilitated via the proposed Cross-Decoder Interaction Block (CDIB), defined as:
2)1 alex CDIB a )
(6)
where i E {1,..., N} indexes the CDIB layers, 2/1 and afex denote the image and text segmentation features at the i-th layer, respectively.
The structure of the proposed CDIB is illustrated in Fig. 2 (right). CDIB consists of two branches: the Image Branch, which is inserted into the intermediate layers of the VAE image decoder, and the Text Segmentation (TextSeg.) Branch, which is inserted into the corresponding layers of the Text Segmentation Decoder. The input features ) and a first pass through two ResBlocks (9. The resulting features are then processed via Ix1 convolution and split along the channel dimension. One half is used for within-branch propagation, while the other half is prepared for cross-decoder feature interaction. The exchanged features are passed through Sigmoid activation and interact with the forward features using Hadamard product, similar in spirit to GLU [24.. but applied in
dual-stream setting. The interaction results are then processed by GroupNorm (36]. SiLU activation
[8. and another Ix1 convolution to produce the final feature maps for each branch. To stabilize
training, skip connection introduced between the input and output of each branch, with learnable scaling factor initialized to zero for the residual part.
3.4 Loss Function
SR-oriented Loss. For the image super-resolution part in our multi-task learning framework, we constrain the reconstruction loss between the predicted high-resolution image Xn and the ground truth high-resolution image X using a weighted sum of MSE, LPIPS, and a modified Focal (MF)
loss:
fing
| x 112 + A ( xH) + Xa latt
(7)
where ||||| denotes the &2 norm, and A 5.0, A2 10.0 are balancing coefficients for different loss terms. To enhance the correlation between text structure preservation during image super- super-resolution and text segmentation, we modify the focal loss (15) by emphasizing hard boundary pixels during image super-resolution guided by segmentation predictions and ground-truths:
m == [1 - sos (1- o s)]² (V*H VxH)²111
7
(8)
where å o (1 8) o (1 s) represents the probability of correct classification for each pixel, and y a hyperparameter adjusting the weight factor. o denotes the pixel-wise multiplication. || || means the & norm. V denotes the Sobel edge operator [22]. Edge pixels with high probability of correct classification receive lower weights, whereas those with lower probability receive higher weights, ensuring more attention is given to challenging text structure pixels during image super- resolution. This loss term reinforces the structural accuracy of text regions and strengthens the inter-task relationships in our multi-task learning framework.
5

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1. Source Text Patches: CTR Dataset (Text image patches with variable quality)
2. Super-Resolutinn via Real-SR Model (Patches are upscaled before this step to preserve structure)
3. Quality Control: OCR-based Filtering (Only patches with high text fidelity are retained)
4. Text Segmentation via SAM-TS Model (Bilingual model, finetuned on TextSeg and BTS datasets)
5. Reliability Control (Further OCR-based fil- tering to validate seg- mentation mask quality)
Source Background Im- ages: LSDIR Dataset (High-resolution images for background scenes)
(A) Existing Text Seg- mentation Datasets (Ready-to-use image- mask pairs, e.g., Total- Text, TextSeg. BTS)
(B) High-Quality Text Patches with Aligned Segmentation Masks (Paired patches from Step 5)
(C) Text-Free HR Background Images (Images with text filtered by OCR to avoid ambiguity)
Synthesis Step (Random pasting/blending, followed by realistic degradation pipeline)
Final Output
(HR-LR image pairs with multiple text regions and corresponding text segmentation masks)
Figure 3: The proposed data synthesis pipeline, which generates training triplets (xz, XH. s) by blending components from three sources: (a) existing text segmentation datasets; (b) high-quality text patches with aligned masks, produced via an SR-and-filtering pipeline; and (c) text-free HR background images. The synthesized high-resolution images with corresponding segmentation masks then subjected to realistic degradation process to generate the final HR-LR-M L--Mask data triplets.
Segmentation-orienti loss. For the segmentation prediction & and its ground-truth s, we employ a combination of MSE, Focal, and Dice Losses, which are commonly used in segmentation tasks 40];
Lgg : ||s $|1 + A3 FocalLoss($, s) + >a DiceLoss($,, s).
where λ3 10.0, A 1.0. The total loss function is formulated the sum of the above components: Luo = limy + Eveg. enabling simultaneous learning across both domains for mutual enhancement.
3.5 Data Synthesis Pipeline
To enable multi-task learning for text-aware image super-resolution and text segmentation, a dataset consisting of LR images, HR images, and text segmentation maps (x1,, XH, required. While obtaining LR-HR image pairs is easier by applying the Real-ESRGAN (32 degradation pipeline to HR images, acquiring high-quality text segmentation maps is costly in terms of manual annotation. Moreover, existing HR image datasets contain insufficient text-rich samples, whereas OCR datasets with abundant text suffer from inconsistent data quality due to generalization concerns.
As shown in Fig. 3 to solve the dilemma, we propose a data synthesis pipeline that pastes cropped samples from text segmentation datasets onto samples from a high-quality image super-resolutio dataset while simultaneously merging the corresponding ground-trum -tuth text segmentation maps. Due to the limited availability of fine-grained text segmentation annotations, we train SAM-TS [40) using the TextSeg (37) and BTS (38) datasets to enhance its text segmentation capability. We then apply the trained model to infer text segmentation maps for the CTR (3] dataset and filter reliable coaudo-ground-eee -ruth (pscudo-GT) samples by comparing OCR-bases text recognition results between

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Training Details. Our model is built upon the Kolors 266 variant of LDMs and follows tile-based inference strategy similar SupIR 44). The diffusion time step is fixed 200. We train the model using the PyTorch framework with the AdamW W optimizer and a fixed learning rate of 5 x 10 Table 1: Quantitative results the test of our proposed FTSR dataset and the validation set of the Real-CE dataset. The best results are displayed in bold.
Datasets
FTSR-TE (x:4
Real-CR-val (Aligned, x4)
Methods
PSNRT
SSIM
LPIPS
FID
OCR-At
PSNRT
SSIMI
LPIPS]
FID
OCR-AT
R-ESRGAN [2]
22.45
0.660
0.299
65.19
0.565
21.59
0.779
0.139
44.03
0.693
HAT [
24.29
0.696
0.299
66.13
0.578
23.05
0.801
0.127
44,50
0.743
SecSR 35
24.32
0.699
0.191
38.10
0.595
22.11
0.753
0.152
43.17
0.218
SupIR
22.13
0.617
0.283
48.97
0.532
21.04
0.709
0.190
46.77
0.359
OSEDif
24.49
0.709
0.169
32.47
21.15
0.735
0.165
50.21
0.244
MARCONeL 122
23.03
0.661
0.336
74.59
0.467
22.46
0.784
0.151
46.25
0.638
DifITSR [46]
23.59
0.680
0.304
61.65
0.543
21.99
0.777
0.147
47.37
0,582
Ours
25.49
0.736
0.152
32.13
0.662
24.02
0.829
0.100
38.01
0.882
the original images and their segmentation maps. Next, random number of cropped images are pasted onto high-quality HR samples from the LSDIR [13 dataset, with the corresponding text segmentation maps placed on a zero-initialized image of the same size. To ensure synthesis quality, we filter out CTR samples with a small long-edge/cchraater count ratio and super-resolve them with otate-of-the-art Real-SR methods [4. Additionally, avoid label ambiguity, we exclude LSDIR samples that contain text by using an OCR-bosCC filtering step. This process results in HR image-text segmentation pairs, which then degraded by the pipeline of Real-ESRGAN 32 to generate LR images, forming the Full-image Text image Resol-tesooltion (FTSR) dataset suitable for joint learning of Real-SR and text segmentation.
The primary goal of the synthetic data is to approximate the real-world data distribution and guide the model to capture the core ternary relationship between low-resolution image Xt., its high-resolution counterpart Жи,, and the corresponding text mask The model learns a mapping from x,. to x that must preserve sufficient structural fidelity to also predict accurately. From this perspective, while the transition between the pasted text and the background may not always perfectly seamless, it does not fundamentally alter this core learning objective. In principle, our data synthesis pipeline can be casily extended to generate plentiful new samples. For instance, OCR methods can be used to detect text regions in HR datasets, followed by segmentation using SAM-TS. The segmentation results can then be evaluated with OCR to filter accurate samples, which can be integrated into the synthesis process. The scalable synthetic data reduces the dependency on costly real-world data acquisition and provides a robust foundation for the model, which can be further fine-tuned on smaller, real-world datasets for practical deployment.
Experiments
4.1 Implementation Details
Dataset Settings. Our training dataset consists of the proposed synthetic dataset FTSR and the real paired scene text super-resolution dataset Real-CE [18] The FTSR dataset contains total of 50,000 triplets (XL, (X, s), where the first 45,000 triplets are allocated for training, and the remaining 5,000 are used for testing. Since some image pairs in the Real-CE dataset are misaligned [46). we manually filtered out such samples, resulting in 337 training pairs and 189 testing pairs. In this dataset, images captured at a 13mm focal length are used as low-resolution images XL, while those captured at 52mm focal length serve as high-resolution images Xy. The text segmentation ground truth is obtained by applying SAM-TS inference on the 52mm images.
Training is conducted on four H20 GPUs with per-GPU batch size of for 200,000 iterations.
4.2 Performance Evaluation
As shown in Table [ we conduct quantitative comparisons between our proposed method and range of ofate-of-the-art approaches, including GAN-based (R-ESRGAN (2), HAT 4). diffusion- based (SeeSR (35), SupIR [4], OSEDiff [34]), and text-specific super-resolution methods (MAR-
7

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Figure 4: Visual comparison of super-resolution results between previous state-of-the-are and ours on sample drawn from the validation dataset of Real-CE 18] Please note the areas in the boxes.
S Fengtan
R A 39
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Figure 5: Visual comparison of super-resolution results between previous state-of-ther oftthe--rts-- and ours on sample in real-world scenarios captured in this paper. Please note the areas in the boxes.
CONet 12), DiffTSR (46)). For fair comparison, we fine-tune their released pre-trained models on FTSR and Real-CE using the official training code (if provided). Note that MARCONet and DifTTSR are inherently designed for cropped text region super-resolution and exhibit substantial performance degradation when directly applied full-image scenarios. To ensure valid outputs for comparison, we first detect and crop text regions using an OCR model [7]. then paste their outputs into the super-resolved images produced by HAT 4 which preserves non-text fidelity effectively. We benchmark all methods on two datasets: the test split of our proposed FTSR dataset (FTSR-TE) and the validation set of Real-CE (Real-CE-val), where misaligned LR-HR pairs are manually filtered out. All models are evaluated under 4x super-resolution setting. We evaluate all methods using PSNR, SSIM, LPIPS 45). FII [10,, and OCR-based recognition accuracy (OCR-A), providing a comprehensive assessment across pixel accuracy, perceptual quality, and text restoration quality.
8

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Table 2: Ablation study on different configurations of our proposed design. All models are trained on the FTSR training set and evaluated on the FTSR test set.
Settings
PSNR1
SSIM
LPIPS
FID4
OCR-A1
w/o JSD
25.13
0.723
0.161
33.70
0,594
w/o TACA
25.26
0.722
0.157
32.74
0.617
w/o MF Loss
25.28
0.729
0.160
32.73
0.629
Ours
25.49
0.736
0.152
32.13
0.662
For the OCR-A metric, we first detect and recognize text in the GT images using an OCR model, preserving the detected bounding boxes. We then extract corresponding regions from the output of each competing method for comparison, focusing on the accuracy of text recognition. The similarity between the prediction and GT is evaluated using the Levenshtein ratio [43] which is defined as (TeneTpre))))) + (en(rg))
Dist(rpeed: rg)) (Le((((eepppeel + Len(rg)), where Len(.) extracts the string length, Fped denotes the recognition result of competing method, l't represents the recognition result in the GT image, and Dist(-,-) refers to the Levenshtein distance between two strings.
As can be seen, our proposed TADiSR demonstrates overall superiority across pixel-level, perceptual, and text accuracy metrics on both synthetic and real-world datasets. Notably, on the Real-CE dataset, TADiSR outperforms the second-best method (HAT) by large margin of 18.7% % in OCR-based recognition accuracy (OCR-A), highlighting its exceptional capacity in recovering accurate textual content from degraded inputs.
We further conduct visual comparisons on the Real-CE, dataset, as shown in Fig. Due to real- worldw degradations, the input images often exhibit irregular noise, blurred strokes, and stroke adhesions. GAN-based methods, while effective at denoising, fail to recover incorrect text structures, leaving adhesion artifacts. Diffusion-based generic SR methods, though possessing stronger generative capabilities, lack text-aware guidance and thus tend to produce more severe stroke distortions, sometimes resulting in completely unrecognizable patterns. Methods tailored for cropped text regions (e.g., MARCONet, DiffTSR) show better structure restoration for short texts. However, MARCONet enforces hard limit of 16 tokens per text block. As a result, longer regions neglected and actually reconstructed using HAT (in the red box). Moreover, when OCR guidance is inaccurate, these OCR-dependent methods may even generate semantically incorrect characters. DiffTSR, though not subject to hard length constraints, performs reliably only for texts shorter than 8 characters, and also fails to produce valid results for longer sequences. Both MARCONet and DiffTSR rely box-wise inference and post-processing fusion, often introducing visible artifacts at patch boundaries during full-image reconstruction. TADiSR effectively avoids the aforementioned pitfalls, making it more suitable for practical applications.
To further evaluate generalization, collected additional real-world samples using digital camera. As illustrated in Fig. [3] TADiSR delivers comparable super-resolution quality on non-text regions while significantly enhancing blurred and adhesive strokes. Moreover, it is worth noting that our method effectively avoids the severe performance degradation commonly observed in text image super-resolution models when handling vertically arranged text. Addressing this issue in existing methods typically requires additional vertical training samples and longer training durations, whereas our approach naturally circumvents this limitation. Due space constraints, we include more visual results under various bilingual scenarios in the supplementary material for interested readers.
4.3 Ablation Study
To verify the effectiveness of our design, we conducted an ablation study, as shown in Table 2 and Fig. 6 Specifically, we evaluate three ablated variants: (1) removing the Joint Segmentation Decoders JSD), (2) disabling the Text-Aware Cross-Attentes Attention mechanism (w/o TACA), and (3) eliminating the modified Focal Loss introduced in Sec. 3..4 (w/o MF Loss). All variants exhibit performance degradation to varying degrees, with the w/o ISD model suffering the most significant drop, an 11 11.4% decrease OCR accuracy, alongside declines in pixel fidelity and perceptual metrics. From the qualitative results in Fig. 6 we can also observe that removing any of the proposed modules weakens the model's ability to reconstruct accurate text structures. The absence of Joint Segmentation Decoders deprives the model of explicit structural supervision, making it learn text shapes in a less

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Figure 6: Visual results of the ablation study under different configurations. Note the zoom-in regions, where our full model achieves more accurate text super-resolution with clearer structural fidelity compared to its ablated variants. Predicted text segmentation masks are also provided for reference.
guided and inefficient manner. Without Text-Aware Cross-Attention, the predicted segmentation masks erroneously highlight irrelevant non-text regions, which mislead the model during training and impair its structural reconstruction ability. The modified Focal Loss, which serves couple the segmentation and super-resolution outputs, encourages the model to focus more on structurally ambiguous regions around text strokes. Eliminating this loss leads to noticeably blurrier and less well-defined text structures, which also hinders the accuracy of the text segmentation predictions.
Overall, the ablation results confirm that each component in our framework directly supports better text structure restoration. Removing any of them leads to noticeable drops in both visual fidelity and quantitative performance, confirming the practical effectiveness of our design choices.
5 Conclusion
In this paper, we proposed TADiSR, a text-aware diffusion model designed for real-world image super-resolution with joint text segmentation decoding. By finctuning the cross- cross-attention layers within the LDM, our approach encourages the model to pay more attention to text-rich regions. Furthermore, the introduction of cross- cross-decoder interactions allows structural text information to be shared between the super-resolution and segmentation decoding branches, resulting in high-quality image outputs with accurate and well-preserved text structures. Extensive experiments on both synthetic and real-world datasets demonstrate the overall superiority of our method in terms of both quantitative metries and visual fidelity. There are also several intriguing directions that merit further investigation, such as extending cross-attention tuning strategies to other rare semantic categories to improve the LDM's generation accuracy, and developing dedicated diffusion-based text segmentation frameworks. Additionally, while TADiSR shows superior performance, there remains significant room for improvement. For instance, collecting more real-world paired data across varied focal lengths, conducting more precise text segmentation annotations, or incorporating stronger priors that explicitly link pure text representations to stylized text appearances could further enhance its performance and generalization in real-world scenarios. Overall, our method offers a feasible and efficient solution for full-image text-aware super-resolution, and we hope it draws greater attention from the community to this practically important but underexplored area.
Acknowledgement
We would like to thank Jinwei Chen in vivo for the insightful discussions and feedback. This work was partially supported by the National Natural Science Foundation of China under Grant no. 62372251.
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A FTSR Synthetic Dataset Visualization
To further illustrate the construction and diversity of our proposed dataset FTSR, we present several example triplets (X,, x s) in Fig. 7 Our proposed synthesis pipeline allows for free composition between background images and foreground text regions. Moreover, we leverage existing text segmentation dataset samples to further train text segmentation model, which is then used to infer on large-scale text recognition datasets. We thereby extract a substantial number of text region images with accurate segmentation masks using OCR--aseed filtering method. As a result, the samples feature rich and varied background contexts, diverse font styles, and accurate pixel-level text segmentation masks, offering supervision for joint learning of super-resolution and segmentation tasks.
B Text Segmentation Comparison
In Fig. 8. we provide a qualitative comparison between our TADiSR model and a state-of-the-art segmentation method Hi-SAM on real-world degraded samples collected in this paper by digital camera. Despite that part of our training segmentation labels were selected from Hi-SAM outputs, our model still delivers significantly finer and more precise segmentation results than those of Hi-SAM across varied scenes. This highlights the strong mutual benefits between text-aware super-resolution and text segmentation tasks, and further validates the effectiveness of our joint learning strategy.
C Extended Visual Comparisons on Real-World Samples
We provide additional visual comparisons between our method and previous ofate-of-the-art ap- proaches in Figs. 12 using real-world samples collected in this paper in the wild. To compre- hensively assess the generalization capabilities of each method, the examples span diverse and challenging scenarios including overexposed neon signs at night, long horizontal text, vertically engraved characters, and handwritten text. In Fig- [ overexposure leads to severe character merging in neon signs, a situation where existing methods struggle to recover legible text. In contrast, our method successfully reconstructs distinct character structures, also showing superior performance on small-scale text. Fig. I0 illustrates results on long horizontal text. While GAN-based models largely preserve structure but fail to enhance quality, diffusion- generic SR methods introduce structural distortions due to lack of text awareness. MARCONet [2] fails to generate valid outputs on long text due to its hard length constraint, with most content falling back to HAT outputs. Similarly, DifTTSR [46] struggles with long sequences, and even in valid predictions (green boxes), character sticking occurs. Our model, however, improves visual quality while maintaining accurate character shapes throughout. In Fig. [ which contains vertically engraved characters, both GAN and generic diffusion methods suffer from reduced legibility and structural distortions. Text image SR methods generally fail due to the vertical layout. Our method, benefiting from global text- text-awareness produces sharp and coherent results, enhancing faint strokes. For handwritten text in Fig. [2] other methods result in missing or merged strokes, while our approach accurately reconstructs the undermined handwritten structure. Additionally, we present more results on the Real-CE dataset in Figs. [3] and 14 which focus on text-rich scenarios like poster and book covers. Our model consistently restores fused strokes and outputs structurally accurate super-resolved results, even for fine-grained characters.
D Limitation Analysis
As illustrated in Fig. [5] we showcase a challenging example from the Real-CE dataset. In our result, the character highlighted by the red circle exhibits a structural discrepancy when compared with the ground truth. This particular character has a high stroke density, and the input image, the strokes are heavily fused due to real-world degradation, making it difficult visually discern the original structure-even for a human observer. Consequently, all methods, including text image super-resolution approaches (within their limitation of text length), fail to predict the correct structure of this character. Future work could explore interactive strategies such as text mask editing or user-guided correction to address such hard cases. Nonetheless, is worth emphasizing that apart from this one character, our method consistently outperforms previous approaches across the rest of the bilingual text in the image, producing clearer and more accurate structural reconstructions.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: In the abstract, we summarize our main claims and the scope of research. In the introduction, we clearly state our contributions and validate them with detailed experiments in Sec.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: We have discussed the limitations in supplementary materials.
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Answer: [Yes]
In the Experiments section, we provide detailed description of the configura- tion for the main experimental results of the paper. And provide code implementation in the supplementary material, ensuring the reproducibility of the main experimental results.
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5. Open access to data and code
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Question: Does the paper specify all the training and test details (c.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary understand the results?
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Justification: the Experiments section, we provide detailed descriptions of all the training and testing details required.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
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Justification: The methods in this field typically do not introduce error bars, and we follow the setting for fair comparisons.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time execution) needed reproduce the experiments?
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Answer: [Yes]
Justification: In Sec. 4.1, we provide detailed information on the computer resources required for the experiments.
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics
Answer: [Yes]
Justification: Our research adheres the NeurIPS Code of Ethics in every aspect.
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: We discuss the positive societal impact of our work in both the Introduction and Conclusion sections, and the problem typically does not involve negative societal impacts.
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: Our paper poses no such risks.
12. Licenses for existing assets
Are the creators original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: The assets (e.g., code, data, models) used in the paper respect the licenses and terms of use of their creators or owners.
13. New assets
Question: Are assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer:
[Yes]
Justification: We detailed our data sources and synthesis methodology in See. 3.5 of the main paper.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [NA]
Justification: The paper does not involve crowdsoureing nor research with human subjects. 15. Institutional review board (TRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: The paper does not involve crowdsourcing research with human subjects.
16. Declaration of LLM usage
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Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [NA]
Justification: The LLM is used only for writing, editing, formatting purposes.
22


Reviews:
[{"review_id": "D3ZiOcVZoy", "reviewer": "Reviewer_oXY4", "summary": "The authors propose a Text-Aware Diffusion model for real-world image super-resolution, named TADiSR. Built upon diffusion models, TADiSR integrates Text-Aware Cross-Attention (TACA), a Text Segmentation Decoder (JSD), and Cross-Decoder Interaction Blocks (CDIB) to enhance text extraction and restoration explicitly. The method significantly improves text restoration performance in images.", "questions": "See weaknesses.", "limitations": "The authors have discussed the limitations.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n1. TADiSR adopts a dual-branch design to extract text information, thereby enhancing text restoration explicitly. The idea is well-motivated and yields strong performance.\n2. The authors also construct a dedicated pipeline for dataset generation, which supports research in this area.\n3. Both quantitative and qualitative results demonstrate excellent restoration performance, which is particularly impressive given the tendency of diffusion models to produce hallucinated textures.\n4. The ablation study validates the contributions of the JSD and TACA modules, and visual comparisons further support these findings.\n5. The paper is well organized. The authors also release code, making the work more solid and reproducible.\n\n\n\n### Weaknesses\n\n1. Although the ablation study validates some components, it does not cover the full model. For example, the impact of CDIB is not studied. The study focuses only on network components, while the proposed pipeline is not experimentally evaluated. The authors should validate the effectiveness of the pipeline itself.\n2. The pipeline section lacks a flowchart, making it harder to understand. Moreover, pasting text onto other images may not accurately reflect real-world scenarios.\n3. In the visual comparisons, the proposed method shows strong performance on Chinese text, but the results of English text are not much different from those of other methods. It is unclear whether this is due to training bias. If other methods like DiffTSR were trained on the same dataset, would the performance gap remain?", "was_revised": true, "final_justification": "The proposed text SR method is effective and novel (also supported by other reviewers). Although there are some limitations in the method design or task formulation, I think the paper still deserves a score of 5.", "rebuttal": "Thanks for your diligent review and the positive feedback on the innovation and performance of our work. We appreciate the opportunity to address the reviewer's questions and provide further clarification as below.\n\n**Q1: The ablation study should cover the full model. The impact of CDIB should be studied. The authors should validate the effectiveness of the pipeline itself.**\n\n**A1:** Thank you for the suggestion. To maintain the clarity of the main paper, our initial ablation study focused on primary contributions. As requested, we have conducted additional experiments targeting the Cross-Decoder Interaction Block (CDIB) to provide a more comprehensive analysis.\n\nFirst, we present the quantitative results for these new ablation settings on the FTSR-TE dataset below:\n\n| Settings | PSNR↑ | SSIM↑| LPIPS↓| FID↓  | OCR-A↑ |\n| -------- | --------- | --------- | -------- | ------- | ------- |\n| Exp. 1  | 25.24  | 0.724   | 0.157 | 33.49 | 0.603 |\n| Exp. 2  | 25.15  | 0.722   | 0.159 | 33.81 | 0.595 |\n| Ours    | **25.49** | **0.736** | **0.152** | **32.13** | **0.662** |\n\nBelow, we detail the setup and observations for each experiment:\n\nExperiment 1: Disabling Feature Interaction within CDIB\n\n- Setup: In this experiment, we kept the CDIB modules in the architecture but disabled the cross-decoder feature exchange mechanism within them. Each branch (image and segmentation) of the CDIB processed its features independently without receiving features from the other.\n- Observation:  This led to a noticeable degradation in text structure fidelity. While the model could still perform both tasks in parallel, the synergy was lost. As the table shows, all metrics declined compared to our full model. The resulting segmentation masks were less accurate, and text in the super-resolved output appeared blurrier. The OCR-A score dropped significantly, confirming that the interactive feature fusion within CDIB is crucial for the mutual enhancement of the two decoders.\n\nExperiment 2: Removing the CDIB Modules Entirely\n\n- Setup: We removed the CDIB modules from the architecture altogether. The image and segmentation decoders operated as two completely separate, parallel pathways from the latent code, with no points of interaction.\n- Observation: This configuration performed worse than Experiment 1 and approached the performance of the \"w/o JSD\" variant from our main paper. Without any architectural connection to facilitate information sharing, the model struggled to leverage segmentation priors to guide text reconstruction effectively. This result underscores the necessity of the CDIB module itself as the structural bridge enabling our joint decoding framework to succeed.\n\n**Q2: The pipeline section lacks a flowchart.**\n\n**A2:** Thank you for this valuable suggestion. A flowchart would significantly improve the clarity of the data synthesis pipeline. As it is not feasible to add graphical figures during the rebuttal phase, we have prepared a text-based flowchart below.\n\n```Plain\n[START]\n   |\n   v\n\n 1. Source Text: CTR Dataset\n    (Text Image Patches with variable quality)\n\n   |\n   v\n\n 2. Super-Resolution via Real-SR Model\n    * Note: To preserve structure, patches are upscaled BEFORE this\n      SR step.\n\n   |\n   v\n\n 3. Quality Control: OCR-based Filtering\n    * Action: Compare text content of original vs. SR patch.\n    * Criteria: Keep only pairs with minimal edit distance to avoid\n      introducing distortions.\n\n   |\n   v\n\n 4. Text Segmentation via SAM-TS Model\n    * Note: This ensures masks are generated for the final, high-\n      quality text, guaranteeing perfect alignment.\n\n   |\n   v\n\n 5. Reliability Control: Further OCR-based filtering to validate\n    the quality and accuracy of the generated segmentation masks.\n\n   |\n   v\n\nThe synthesis process combines three distinct data sources:\n\n    (A) High-Quality Text Patches with Aligned Segmentation Masks\n        (The output from Step 5 above)\n\n    (B) Existing Text Segmentation Datasets\n        (To enrich variety)\n\n    (C) Clean, Text-Free Background Images\n        (Derived from the LSDIR Dataset after OCR-based filtering to\n        remove any images containing pre-existing text)\n\n    |\n    | All three sources (A, B, C) are fed into the next step\n    |\n    v\n\n 6. Synthesis Step\n    * Method: Random Pasting or Image Blending\n\n   |\n   v\n[FINAL OUTPUT]\nHR Images with Multiple Text Regions and Paired, High-Fidelity Segmentation Labels.\n\n```\n\nWe will ensure that a clear, professionally drawn flowchart is included in the main body of the paper in the next revision.\n\n**Q3: Pasting text onto other images may not accurately reflect real-world scenarios.**\n\n**A3:** We appreciate the valuable question. The primary motivation for proposing the FTSR dataset was the profound scarcity of high-quality, large-scale datasets with bilingual text scenes suitable for image super-resolution. As far as we know, the available resources are either text recognition datasets with inconsistent image quality (e.g., CTR [1]) or text segmentation datasets with limited scene diversity (e.g., BTS [2]). To facilitate dataset scaling, we adopted this simple yet effective heuristic pasting strategy, which allows us to generate data with greater diversity in scenes and text combinations than is currently available.\n\nIt is important to note that, in the short term, the goal of synthetic data is not to perfectly replicate but to approximate real-world data distributions. A model trained on this data can learn the core assumption of our task—the ternary relationship between a low-quality image with text $\\textbf{x}_L$, its high-quality counterpart $\\textbf{x}_H$, and the corresponding ground-truth segmentation mask $\\textbf{s}$, where the mapping from  $\\textbf{x}_L$ to $\\textbf{x}_H$ must restore sufficient text-structural fidelity to accurately predict $\\textbf{s}$.  From this perspective, whether the transition between the pasted text patch and the background is perfectly seamless does not fundamentally alter this core relationship. The primary purpose of this large-scale synthetic data is to reduce the dependency on vast amounts of real-world data and therefore lower data acquisition costs.  However, for practical deployment, a final fine-tuning step on a smaller set of real-world data is always anticipated.\n\nIn the long term, the ideal synthetic dataset should indeed simulate the real world as closely as possible to further narrow the gap and reduce the need for real-world fine-tuning. Our current strategy can certainly be improved. We have identified two promising directions for future work:\n\n1. Image Harmonization: Employing techniques like Poisson blending to create a smoother, more natural transition between the text patches and the background images.\n2. Text Image Rendering based on Generative Models: Using text-to-image models with strong text rendering capabilities (e.g., JoyType [3], AnyText [4], Seedream [5]) to generate entire scenes. \n\nWe have conducted preliminary experiments based on both of these approaches:\n\nBy applying Poisson blending to a subset of our training data, we observed a marginal improvement in perceptual quality, although the impact on quantitative metrics like PSNR and OCR-A was minimal. This suggests that while harmonization can enhance visual appeal, our core pipeline already provides the essential structural information needed for effective training. \n\nRegarding generative text image rendering methods, our specific experimental findings are as follows:\n\n- JoyType relies on manually rendered text outlines, which makes it challenging to scale for large-scale dataset generation.\n- AnyText requires manual specification of text placement and frequently produces instances with missing or distorted strokes, or text that does not strictly adhere to the prompt. This limits its practical applicability for high-quality ground-truth generation.\n- Seedream 3.0 is more promising, as it can generate high-resolution, bilingual images that largely conform to text prompts without requiring manual text placement. However, in our experiments, we still encountered issues such as text positioning inaccuracies relative to the prompt, structural distortions in small characters, and sharpening artifacts at text edges. These issues could potentially compromise the naturalness and fidelity of the text regions in full-image super-resolution results.\n\nIn summary, current generative text image rendering models are still some distance from consistently producing high-quality, full-image text ground-truths suitable for large-scale SR training. We will add the discussion in the next version of our paper to thoroughly evaluate the applicability of different data synthesis pipelines for full-image text super-resolution data generation.\n\nThank you again for your constructive feedback. We hope our responses have addressed your concerns.\n\n**References**: \n\n[1] Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study. \n\n[2] A Bi-Lingual Benchmark for Text Segmentation in the Wild.\n\n[3] JoyType: A Robust Design for Multilingual Visual Text Creation.\n\n[4] AnyText: Multilingual Visual Text Generation And Editing. \n\n[5] Seedream 3.0 Technical Report."}, {"review_id": "SE23mz2FTE", "reviewer": "Reviewer_GxHi", "summary": "This work proposes a novel framework to address the issue that unnatural details of text in image generated by diffusion model, which is practical and  effective.", "questions": "Please refer to the weakness.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strength**:\n1. The motivation is strong and novelty. It is an important issue that unnatural details of text in image generated by DM, especially for Chinese character.\n2. The design of the devised frame work is simple yet effective. The experimental results demonstrate the effectiveness of the proposed framework, which deliver clear and realistic text in the generated image.\n\n**Weakness**:\n1. The confused name of the decoder for segmentation. The joint seg decoder is introduced to generate segmentation map for text in the image. However, it is not used to generate SR image at the same time, therefore, the joint is not proper, which would cause confusion.\n2. Unclear explanation in Figure 2. For example, there is no explanation about the orange squares, which could be the LoRA?\n3. Confusing descriptions. In line 150-151, why does the $c_{tex}$ need to be located? Isn't it the last one in the $c_y$?\n4. Unclear motivation of design for $a_{tex}$. Why are $a_{tex}$ concatenated along the channel dimension and how can it be used to generate the segmentation map solely without $z_H$?\n5. It would be better to discuss the parameters, FLOP, and inference time compared to other methods by introducing additional VAE decoder.", "was_revised": true, "final_justification": "The proposed text SR method is is practical and  effective. Although there are some limitations in the method design or task formulation, I think authors further improve the quality of the paper and the quality paper still deserves a score of 5.", "rebuttal": "Thanks for the diligent review and positive feedback on the novelty and design of our work. We appreciate the insightful questions, which help us improve the clarity of our paper. We address each of your points below.\n\n**Q1: The name of the joint seg** **decoder** **is confusing.**\n\n**A1**: Thank you for pointing this out. As mentioned on Line 38 of the main text, \"Joint Segmentation Decoders\" is an abbreviation for \"joint image–text segmentation decoder.\" To eliminate any potential ambiguity and make this clearer, we will revise the figure to explicitly label the two parallel branches as the \"Image Decoder\" and the \"Text Segmentation Decoder.\" We apologize for any confusion this may have caused.\n\n**Q2: There is no explanation about the orange squares, which could be the LoRA**?\n\n**A2:** Your assumption is correct; the orange squares indeed represent the LoRA adapters. In Figure 2, we have indicated this with the label \"LoRA Adapters\" positioned above the U-Net architecture. We adopted this visualization style as it is a common convention, similar to the approach used in prior works such as Image2Image Turbo [1]. We will ensure this label is more prominent in the revised version to avoid any misunderstanding.\n\n**Q3: Why does the** $c_{tex}$ **need to be located?** **Isn't it the last one in the** $c_y$**?**\n\n**A3:** Your understanding is correct for our current implementation. The term \"locating\" refers to the process of acquiring the specific cross-attention map corresponding to the \"text\" token. In our current setup with a fixed prompt, this is indeed achieved by simply accessing the token at a known, fixed index.\n\nHowever, we used the more general term \"locating\" to maintain broader applicability for future work. This phrasing accommodates potential extensions to dynamic prompts, where the position of the \"text\" token might vary and would require an explicit search or location step. This choice of terminology was intended to keep the description general rather than tying it to our specific, fixed-prompt implementation.\n\n**Q4: Why are** $a_{tex}$ **concatenated along the channel dimension and how can it be used to generate the** **segmentation** **map solely without** $ z_H$**?**\n\n**A4:** Thank you for this insightful and detailed question. It has two parts, which we will address in order.\n\n1. **Why Concatenate?** As we mentioned in our preliminary experiments (Line 28), techniques like DAAM have shown that aggregated cross-attention maps hold strong spatial semantic information that can be guided to focus on specific regions (in our case, text). DAAM typically achieves this by upsampling and summing the maps. We reasoned that concatenating these maps along the channel dimension, rather than summing them, would be a more lossless approach. This method preserves the distinct information from each attention layer, creating a rich feature representation that serves as the initial latent input for the text segmentation task.\n2. **How it works with** $z_H$**:** Crucially, the segmentation map is not generated *solely* from these attention maps. This addresses the second part of your question. Our architecture facilitates interaction with the image latent $z_H$. This is achieved through our proposed Cross-Decoder Interaction Blocks (CDIBs). As detailed in the paper, CDIBs are dual-stream blocks that enable the exchange of features between the image decoding branch (processing $z_H$) and the text segmentation branch (processing $a_{tex}$). This interaction allows the segmentation decoder to be informed by the fine-grained spatial details from $z_H$, leading to a precise and contextually aware final segmentation map.\n\n**Q5: It would be better to discuss the parameters, FLOPs, and inference time compared to other methods by introducing an additional VAE decoder.**\n\n**A5:** Thanks for the suggestion. We provide the table below containing a comparative analysis against standard diffusion model adaptation techniques. All metrics were measured on a single NVIDIA H20 GPU with an input resolution of 512x512 pixels.\n\n| Model Configuration | Extra Trainable Params | FLOPs (GFLOPs) | Inference Time (ms) |\n| ------------------- | ---------------------- | -------------- | ------------------- |\n| SDXL Baseline       | -                      | 2630.49        | 211.19              |\n| SDXL + LoRA         | ~12.61 M               | 2652.47        | 260.85              |\n| SDXL + ControlNet   | ~1263.01 M             | 3006.68        | 260.31              |\n| Ours (TADiSR)       | ~48.29 M               | 4497.96        | 342.32              |\n\nHere is a breakdown of the comparison:\n\n- **Comparison with LoRA:** Our method introduces approximately 35.68M extra trainable parameters beyond a standard LoRA setup, primarily from the new segmentation decoder and the Cross-Decoder Interaction Blocks (CDIBs). While this is a modest increase in parameters, the additional FLOPs and a slight increase in inference time are a direct result of the parallel decoding process. We believe this is a very reasonable trade-off for the substantial improvement in text fidelity, a capability that standard LoRA fine-tuning alone cannot provide.\n- **Comparison with ControlNet:** Our approach is significantly more parameter-efficient. A standard ControlNet adds over a billion parameters by duplicating the U-Net encoder blocks. In contrast, our method achieves fine-grained structural control over text regions with only a fraction of the parameters (~3.8% of a standard ControlNet) and a manageable increase in inference latency. This highlights the efficiency of our joint decoding design for the specific task of text-aware super-resolution.\n\nIn summary, while the multi-scale feature interactions within our Joint Segmentation Decoders introduce a certain computational overhead, the resulting increase in latency is acceptable. The reason a large model like ControlNet can be relatively fast is that it operates on a single scale within the latent space. However, this single-scale manipulation is often insufficient for restoring heavily degraded text details, which demands the multi-scale, fine-grained feature processing that our architecture provides.  \n\nAs this is one of the first works to tackle full-image text super-resolution, our primary focus has been on maximizing model performance and restoration quality. We acknowledge that for practical deployment, further optimization techniques could be applied. We will explore these avenues and discuss them in detail in the next version of our work.\n\n[1] One-Step Image Translation with Text-to-Image Models."}, {"review_id": "PmM5RXQ5WR", "reviewer": "Reviewer_AA8X", "summary": "The paper proposes a diffusion-based SR framework TADiSR to solve the textual structure distorting issues commonly appearing in generative models. TADiSR introduces a unified framework that integrates text-aware attention and dual-stream decoding to recover degraded text in natural images.  **[Network]** The proposed fine-tuning of cross-attention between the word \"text\" and image tokens effectively guides the diffusion models to pay attention to text regions. Meanwhile, the joint image-text segmentation decoder enables more accurate and structure-preserving restoration of textual contents, in a simultaneous generation way. **[Data]** In addition to the network design, the data synthesis pipeline further supports effective training with fine-grained text masks and diverse backgrounds with high quality, making the approach scalable in both Real-SR and text segmentation. **[Loss]** To enhance the preservation of text structures, the paper introduces a modified focal loss that emphasizes hard boundary pixels by leveraging segmentation predictions and ground-truths. TADiSR is compared with GAN-based methods, diffusion-based methods, and SOTA text image SR methods, on both synthetic and real benchmarks, where improved results are shown quantitatively and qualitatively, demonstrating its superiority in both visual perception and text fidelity.", "questions": "1. The paper lacks ablation studies to validate the effectiveness of the FTSR dataset. Could the authors evaluate whether training with FTSR improves performance on Real-CE-val? It would also be helpful to show whether other baseline methods benefit from using FTSR to demonstrate its general utility.\n1. Since the CTR dataset mainly contains Chinese text, could the improved performance be partly due to exposure to Chinese characters during training, which competing methods lack? It would be important to show that the performance gains are not solely from language-specific data but also from other proposed designs (e.g., JSD, TACA, MF loss).  Also, it is suggested to provide more examples on other languages in addition to Chinese to show its overall ability.\n1. The evaluation of text recovery primarily relies on a single metric, OCR-A, which may not sufficiently capture the full quality of restored text. Incorporating additional metrics such as OCR-based $F_1$ scores [1] or other metrics would provide a more comprehensive and robust assessment of text fidelity.\n1. It would be helpful to include a table comparing inference speed, memory usage, and model size across all methods. This would provide a clearer picture of the practical trade-offs and deployment feasibility of TADiSR.\n---\n[1] Chee Kheng Chng, Yuliang Liu, Yipeng Sun, Chun Chet Ng, Canjie Luo, Zihan Ni, ChuanMing Fang, Shuaitao Zhang, Junyu Han, Errui Ding, et al. Icdar2019 robust reading challenge on arbitrary-shaped text-rrc-art. In 2019 International Conference on Document Analysis and Recognition (ICDAR), pages 1571–1576. IEEE, 2019.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Paper strengths**\n\n1. The method shows clear advantages in both quantitative and qualitative evaluations. In particular, the qualitative results are impressive, with visually natural and structurally accurate text restoration, significantly outperforming existing methods in preserving the fidelity of textual content.\n1. The integration of text-aware cross-attention supervision with dual-stream decoding in a diffusion framework is a novel contribution. Moreover, the insight that text-aware super-resolution and text segmentation are complementary tasks that can be effectively unified under a multi-task learning paradigm adds depth and originality to the work.\n1. The paper is clearly written and well structured. The methodology is explained in sufficient detail, and the model architecture is effectively illustrated with clear figures. \n\n**Major weaknesses**\n\n1. One major concern lies in the data synthesis pipeline used to construct the FTSR dataset. The copy-and-paste strategy, while effective for generating large-scale training data, may introduce a distribution gap between synthetic images and real-world scenes, potentially limiting the model’s generalization ability. It remains unclear whether the authors have made any attempts to mitigate this domain gap or assess its impact.\n1. Another concern is the potential quality issue introduced during data synthesis, specifically in the step where text samples are first super-resolved using a Real-SR method before being pasted onto high-resolution backgrounds (as mentioned in line 217). This process may introduce distortions into the text regions, lowering the ground truth quality. Moreover, since the text segmentation masks are generated from the original CTR data, they may no longer perfectly align with the super-resolved text patches, leading to mismatches between the image content and segmentation labels during training.\n1. While the Joint Segmentation Decoders offer explicit structural supervision through cross-decoder interactions, it is unclear how this dual-decoder design affects inference efficiency and memory usage. The potential computational overhead introduced by maintaining and interacting between two decoding branches may hinder the model’s practical deployment, yet the paper does not provide a detailed analysis of its impact on inference speed.\n\n**Minor weaknesses**\n\n1. Fig 4. The cropped patch in the boxes of GT is slightly misaligned.", "was_revised": true, "final_justification": "The authors have fully addressed my concerns during the rebuttal, and thus I decide to raise my score to **Boarderline accept**.", "rebuttal": "We sincerely thank the reviewer for their insightful comments and high praise for our model's advantages, particularly its visual results and novelty. We will now address each of your questions in detail.\n\n**Q1: It remains unclear whether the authors have made any attempts to mitigate the domain gap between the synthetic and real-world data pairs.**\n\n**A1:**  Thank you for this insightful question. We have provided a detailed analysis on this point in our response to Reviewer oXY4, Question 3. Due to space limitations, we kindly ask you to refer to that response for a comprehensive explanation. We appreciate your understanding.\n\n**Q2: The super-resolution of text samples may introduce distortions into the text regions. Moreover, the text segmentation masks may no longer perfectly align with the super-resolved text patches.**\n\n**A2:** Thank you for your meticulous observation. Our data pipeline in the main paper needs to be improved. It is crucial to note that most publicly available Chinese text datasets are designed for text recognition, and their image quality is often inconsistent, containing noise, artifacts, and other degradations. Training directly on such data would teach the model to ignore these degradations rather than correct them.\n\nTo prevent the Real-SR method from introducing distortions, we implemented two key quality control measures during data synthesis:\n\n1. **Upscaling Before Super-Resolution:** We first enlarge the scale of the text image patches before applying the super-resolution model. This ensures that the structure of the text, which now occupies a larger portion of the patch, is better preserved during the SR process.\n2. **OCR-based Filtering:** To avoid distortions in smaller text, which can negatively impact training, we use an OCR detector to extract text from both the original and the super-resolved patches. We only include samples in our training set where the edit distance between the two versions is minimal. This process effectively guarantees the quality of the text regions.\n\nWe agree that the Super-Resolution step should indeed precede Text Segmentation  Prediction to ensure perfect alignment between the final image content and the segmentation masks. As we are unable to upload images during this rebuttal phase, we have provided a corrected text-based flowchart of our data synthesis pipeline (Please refer to R1A2 due to the character limitation) and will include a properly rendered diagram in the next version of the paper.\n\n**Q3: Please provide a detailed analysis of the impact of the dual-decoder design on inference speed.**\n\n**A3:** Thanks for the suggestion. We provide the analysis in A2Q5 due to the character limitation.\n\n**Q4: Could the authors evaluate whether training with FTSR improves performance on Real-CE-val?**\n\n**A4:**  To demonstrate the effectiveness of our proposed FTSR dataset, we have conducted an ablation study comparing the performance of both our model and the OSEDiff baseline on the Real-CE validation set, with and without pre-training on FTSR, as shown in the following table: \n\n| Methods | Settings | PSNR↑ | SSIM↑ | LPIPS↓ | FID↓  | OCR-A↑ |\n| ------- | -------- | ----- | ----- | ------ | ----- | ------ |\n| OSEDiff | w/o FTSR | 21.04 | 0.716 | 0.178  | 51.46 | 0.223  |\n| OSEDiff | w/ FTSR  | 21.15 | 0.735 | 0.165  | 50.21 | 0.244  |\n| Ours    | w/o FTSR | 23.55 | 0.813 | 0.097  | 32.83 | 0.713  |\n| Ours    | w/ FTSR  | 24.02 | 0.829 | 0.1    | 38.01 | 0.882  |\n\nAs the results show, pre-training on FTSR improves the performance of both models on the real-world Real-CE dataset across various metrics. This underscores the necessity of incorporating large-scale synthetic data and validates the effectiveness of our data synthesis strategy.\n\n**Q5: Whether other baseline methods benefit from using FTSR to demonstrate its general utility. Could the improved performance be partly due to exposure to Chinese characters during training, which competing methods lack?**\n\n**A5:** In our submitted version, for diffusion-based models with publicly available training code, such as SeeSR and OSEDiff, we followed the exact same pre-training and fine-tuning procedure as our own method (as described in Line 245 of the main paper). This includes pre-training on our synthetic FTSR dataset.\n\nFurthermore, the table in **A4** reveals another important insight: while baseline models without text-aware capabilities do benefit from FTSR, their performance gain—especially on text-related metrics like OCR-A—is significantly smaller than ours. For instance, OSEDiff's OCR-A improvement from FTSR is only 12.4% of the improvement seen by our model. This disparity strongly suggests that the performance gap is not merely due to data exposure but is fundamentally driven by our model's architecture. The superior ability of our model to leverage the FTSR dataset highlights the importance of its text-aware design and validates the rationality and efficiency of our architectural choices for full-image text super-resolution.\n\n**Q6: It is suggested to provide more examples** **on** **other languages in addition to Chinese to show its overall ability.**\n\n**A6:** Thank you for the suggestion. In fact, our experimental setup is already bilingual. Both the CTR dataset (used for synthesizing the training set) and the Real-CE test set contain bilingual (Chinese and English) samples. Examples of this can be seen in Figure 1 and Figure 4 of the main paper, with additional English-centric samples in Figure 8 and Figure 9 of the supplementary material.\n\nIt is worth noting that the reconstruction of English text is significantly less challenging than that of languages with complex stroke-based characters like Chinese, due to the limited number of letters in the English alphabet. A simple fine-tuning of a base model (like SDXL or SD3) can achieve considerable performance in English-only scenarios. Our research was motivated by the significant lack of attention in the academic community to super-resolution tasks for these more complex languages.\n\nGiven that we cannot provide additional images during the rebuttal phase, we will include more performance comparisons in English-rich text scenarios (e.g., on a dataset like TeleScope) in the next version of our paper to offer readers a more comprehensive view.\n\n**Q7: Incorporating additional metrics such as OCR-based F1 scores or other metrics would provide a more comprehensive and robust assessment of text fidelity.**\n\n**A7:** We appreciate this constructive feedback. In the experiments in our main paper, we initially chose to perform text detection on the ground-truth (GT) images first and then use those bounding boxes to evaluate recognition on the predicted images. This was done to isolate the text recognition capability from potential errors in the OCR model's text detection stage. However, as the reviewer correctly implies, this approach causes the Precision, Recall, and F1-score to converge to the same value, which is why we did not report them separately.\n\nIn response to the reviewer’s suggestion, we have now conducted a full, standard OCR evaluation for every method. This process includes both text detection and text recognition on the output images, where predicted boxes are matched to GT boxes based on IoU and text similarity thresholds. The resulting Precision, Recall, and F1-Score are reported below:\n\n| Metric        | R-ESRGAN | HAT    | SeeSR  | SupIR  | OSEDiff | MARCONet | DiffTSR | **Ours (TADiSR)** |\n| ------------- | -------- | ------ | ------ | ------ | ------- | -------- | ------- | ----------------- |\n| **Precision** | 0.6672   | 0.6995 | 0.3027 | 0.3834 | 0.2772  | 0.6602   | 0.5771  | **0.7871**        |\n| **Recall**    | 0.6147   | 0.6773 | 0.1955 | 0.3274 | 0.2242  | 0.5855   | 0.4995  | **0.8097**        |\n| **F1-Score**  | 0.6399   | 0.6882 | 0.2376 | 0.3532 | 0.2479  | 0.6206   | 0.5355  | **0.7983**        |\n\nAs can be seen, our method remains state-of-the-art across all three metrics, demonstrating its robust performance advantage in a more comprehensive evaluation setting.\n\n**Q8: It would be helpful to include a table comparing inference speed, memory usage, and model size across all methods.**\n\n**A8:**  Thank you for this practical suggestion. We have compiled a table comparing the efficiency of our model against several representative methods cited in our paper, including GAN-based and one-step diffusion-based methods. All benchmarks were conducted on an NVIDIA H20 GPU with an input resolution of 512x512 pixels.\n\n| Method        | Model Size (Total / Trainable) | FLOPs (GFLOPs) | Inference Time (ms) |\n| ------------- | ------------------------------ | -------------- | ------------------- |\n| R-ESRGAN      | 16.70 M / 16.70 M              | 4699.75        | 622.57              |\n| HAT           | 20.77 M / 20.77 M              | 6670.32        | 1086.68             |\n| OSEDiff       | 1294.38 M / 244.81 M           | 2160.60        | 215.13              |\n| Ours (TADiSR) | 2711.89 M / 48.29 M            | 4497.96        | 372.32              |\n\nAs the results show, our method demonstrates a highly competitive profile in terms of trainable parameters, FLOPs, and inference time, especially considering the state-of-the-art performance it achieves in full-image text super-resolution. While one-step models like OSEDiff are faster, our approach maintains a practical inference speed with a remarkably small number of trainable parameters, highlighting its efficiency."}, {"review_id": "AsXasT5LlW", "reviewer": "Reviewer_J5Zt", "summary": "- **What**: This paper proposes TADiSR, a text-aware diffusion-based framework that jointly performs image super-resolution and text segmentation, aiming to restore degraded real-world scenes with sharper textual structures.\n- **How**: The method augments a latent-diffusion backbone with (i) text-aware cross-attention, which steers attention toward textual regions, and (ii) joint segmentation decoders that output a super-resolved image and its text mask. Then, the entire model is trained using an SR-oriented loss with edge-aware focal terms and a segmentation loss on the 50k-image FTSR dataset synthesized by a scalable, text-oriented pipeline.\n- **Results**: On FTSR-TE and Real-CE-val, TADiSR shows the best PSNR/SSIM/LPIPS/FID and boosts OCR accuracy by +18.7%p over the previous best method, with ablations showing that removing any main component degrades performance.", "questions": "1. **Exact definition of the w/o TACA setting (Table 2)**:\nWhen Text-Aware Cross-Attention is disabled, which operations are switched off or replaced? More importantly, what feature tensor is forwarded to the Joint Segmentation Decoder? A concise diagram or textual description would make the ablation easier to interpret.\n2. **Report absolute and ΔOCR-A scores alongside transcripts**:\nList the OCR-A score of the raw LR inputs, followed by the gain (Δ) achieved by each method. Following BTC [1] Fig. 3, could you also overlay the recognized text strings on the qualitative comparison figures?\n\n**Impact on score**\nClarifying the ablation protocol and visualizing the OCR results would remove key ambiguities. Convincing answers could raise my overall rating by 1 point.\n\n[1] Pak et al., \"B-Spline Texture Coefficients Estimator for Screen-Content Image Super-Resolution,\" CVPR 2023.", "limitations": "yes.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n1. **State-of-the-art performance**: TADiSR outperforms all published methods on FTSR-TE and Real-CE. It raises OCR-A by 18.7 percentage points over the previous best and improves PSNR, SSIM, LPIPS, and FID simultaneously. This is clear evidence of perceptual and recognizability gains.\n2. **Novel, well-motivated architecture**: The model explicitly couples SR and pixel-level text segmentation by integrating Text-Aware Cross-Attention (TACA) with a Joint Segmentation Decoder. Ablation studies show that disabling either module sharply degrades OCR and structural fidelity, validating the necessity of the design.\n3. **Scalable synthetic data pipeline**: The authors contribute a 50,000-image dataset generated by a flexible, text-oriented synthesis pipeline. This pipeline provides accurate text masks and rich backgrounds. It offers a reusable resource for future text-oriented SR and segmentation work.\n4. **Thorough empirical validation with clear visualizations**: The paper reports five quantitative metrics, diverse qualitative comparisons, and attention heat maps. These results give readers numerical and visual confidence in the method’s effectiveness.\n\n### Weaknesses\n1. **Narrow degradation and scale coverage**: All experiments use a single ×4 setting with relatively mild Real-ESRGAN degradation, and many of the input examples are already human-readable. The paper does not address how the model behaves under heavier blur, noise, or larger factors (e.g., ×6 or ×8).\n2. **Sparse Analysis of Text-Aware Cross-Attention (TACA)**: The ablation table only shows a binary switch for w/o TACA. It does not test directing attention to alternative tokens or to a global text embedding. Therefore, the paper stops short of proving that steering specifically to the word \"text\" is essential rather than incidental.\n3. **Unexplored Prompt Sensitivity**: All results rely on the fixed prompt \"text.\" The impact of alternative wording (\"sign,\" \"label,\" etc.) or prompt-free inference is unreported, leaving deployment robustness uncertain.\n\n*My confidence in the method's generality and practical deployability would increase substantially if the authors (i) added results for stronger and multi-scale degradations, (ii) provided token-level ablations that isolated the benefit of the \"text\" cue, and (iii) tested performance under varied or absent prompts.*", "was_revised": true, "final_justification": "This work has many strengths (see #Strengths), and the authors have thoroughly addressed all of my concerns. I believe it deserves a borderline accept.", "rebuttal": "We are very grateful for the reviewer's encouraging and positive assessment of our work, especially regarding its performance, novelty, and the data synthesis pipeline. The detailed and insightful feedback has been invaluable in helping us to strengthen our paper. We address each concern point-by-point below.\n\n**Q1: The model's performance on more severe degradation and larger upscaling factors (e.g., ×6 or ×8).**\n\n**A1:** We thank the reviewer for the question. We respectfully clarify that up to the ×4 super-resolution setting is a common and established practice in recent state-of-the-art literature for both general real-world SR and text image SR. For instance, methods we compared against, such as HAT, SeeSR, OSEDiff, MARCONet, and DiffTSR, as well as other recent works like CATANet [1] and PFT-SR [2], all present their primary results at a maximum of ×4 magnification. This is because at higher scaling factors, severe information loss in the input makes it exceedingly difficult for any model to reliably reconstruct faithful structures and local details, a challenge that is particularly acute for the intricate structures of text. We chose the ×4 setting in our main paper to ensure a fair and direct comparison with these contemporary methods.\n\nFurthermore, we believe it is important to consider the reliability of results on inputs that are completely illegible to humans. While a model might occasionally generate a correct character from such an input, this result is likely due to statistical guessing or overfitting rather than genuine reconstruction. This may appear impressive but is not always reliable or beneficial for practical applications. For example, as shown in our paper, MARCONet incorrectly reconstructs “清华大学” (Tsinghua University) as “国华大学” (Guohua University) and, in the supplementary material, misinterprets \"Huancheng\" as \"Wuancheng\". Such semantic errors are often less acceptable in real-world use cases than a blurry but structurally correct result.\n\nNevertheless, to address the reviewer's concern, we have conducted additional experiments for the ×8 setting. Since there is a lack of paired real-world text SR datasets for such a high scaling factor (Real-CE only provides up to ×4), we simulated an ×8 scenario by downsampling the 13mm focal length images from Real-CE by an additional factor of 2. We then fine-tuned both OSEDiff and our method on this new ×8 dataset. The results are as follows:\n\n| Method  | Dataset  | PSNR↑ | SSIM↑ | LPIPS↓ | FID↓  | OCR-A↑ |\n| ---| ---| -- | ---| --- | -- | --- |\n| OSEDiff | Real-CE-val (×8) | 21.06 | 0.716 | 0.180  | 51.27 | 0.228  |\n| Ours| Real-CE-val (×8) | 23.43 | 0.813 | 0.095  | 32.63 | 0.651  |\n\nOur method continues to outperform the baseline, demonstrating its robustness even under more severe degradation. In the next version of our paper, we will introduce evaluations and visual comparisons for more methods on higher-scale settings.\n\n\n\n\n\n**Q2: The necessity of the specific word \"text\" and the impact of alternative prompts.**\n\n**A2:** We thank the reviewer for raising this important point. As requested, we conducted experiments using different keywords in the prompt:\n\n| Prompt Keyword | Dataset  | PSNR↑ | SSIM↑ | LPIPS↓ | FID↓  | OCR-A↑ |\n| --- | --- | --- | --- | --- | ---| ---|\n| \"sign\" | Real-CE-val (×4) | 23.57 | 0.812 | 0.095  | 37.46 | 0.871  |\n| \"label\" | Real-CE-val (×4) | 23.71 | 0.816 | 0.094  | 37.18 | 0.875  |\n| \"text\" (Ours)  | Real-CE-val (×4) | 24.02 | 0.829 | 0.100  | 38.01 | 0.882  |\n\nIn fact, the specific keyword is not the most critical aspect. The word \"text\" can be viewed as analogous to a \"trigger word\" in LoRA or a \"pseudo-word\" in Textual Inversion. Its primary function is to activate the specialized knowledge the model acquired during fine-tuning. We chose \"text\" because it is a more general and broadly applicable term for text-related content compared to \"sign,\" \"label,\" or \"writing.\"\n\nWe also plan to explore more advanced prompting strategies in future work, such as embedding the specific textual content or dynamic background descriptions into the prompt to further enhance quality. However, this remains a significant challenge for current diffusion foundation models, as none can yet reliably generate long, coherent strings of text from a prompt, which is why we adopted the current, more robust prompting strategy.\n\nRegarding prompt-free inference, this scenario is effectively covered by the ablation study in Q3, \"disabling the Text-Aware Cross-Attention mechanism\". In that setting, the cross-attention response to the text prompt is completely removed from the text segmentation estimation process. This eliminates the model's explicit text-awareness, leading to weaker quantitative metrics and visibly blurrier text regions, demonstrating that the text preservation capability is not activated.\n\n**Q3: Clarification on the \"disabling the Text-Aware Cross-Attention mechanism\" ablation study.**\n\n**A3:** We apologize for the lack of clarity in our initial description. In the \"disabling the Text-Aware Cross-Attention mechanism\" (w/o TACA) setting, we replace the aggregated text-aware cross-attention feature, $\\textbf{a}_{tex}$, with the denoised image latent feature, $\\hat{\\textbf{z}}_H$, as the input to the Text Segmentation Decoder. This is done to validate the effectiveness of using the specific text-aware cross-attention signal.\n\nAs shown in Table 2 and Figure 5 of our paper, disabling TACA leads to a significant drop in TADiSR's performance, evidenced by weaker numerical scores and blurrier text regions. We will ensure this detailed explanation of the ablation settings is included in the revised version of our paper.\n\n**Q4: The OCR-A score gain (Δ) over the raw** **LR** **inputs.**\n\n**A4:** Thank you for this excellent suggestion. This is a very reasonable way to evaluate performance. We report the OCR-A gain on the Real-CE-val dataset below:\n\n| Methods  | OCR-A | ΔOCR-A |\n| -------- | ----- | ------ |\n| Input    | 0.797 | 0      |\n| R-ESRGAN | 0.693 | -0.104 |\n| HAT | 0.743 | -0.054 |\n| SeeSR| 0.218 | -0.579 |\n| SupIR| 0.359 | -0.438 |\n| OSEDiff| 0.244 | -0.553 |\n| MARCONet | 0.638 | -0.159 |\n| DiffTSR| 0.582 | -0.215 |\n| Ours | 0.882 | +0.085 |\n\nNotably, all prior methods exhibit a decrease in text legibility after super-resolution. This is understandable for general-purpose SR models that lack text awareness. For specialized text SR models like MARCONet and DiffTSR, the performance drop occurs for two main reasons: 1) they can mis-recognize a character, causing a correctly identified character in the LR input to become incorrect, and 2) these patch-based methods struggle with long or vertically-oriented text, leading to performance degradation in real-world scenes. As described in Line 247 of our paper, we already provided these models with text regions pre-cropped by an OCR model; otherwise, they would fail to produce any valid output on full images.\n\nIn contrast,  our method is the only one to achieve a positive gain, improving the OCR accuracy by 10.7% over the raw input. This underscores the significance of our full-image, text-aware super-resolution approach.\n\n**Q5: Overlaying recognized text strings on the qualitative comparison figures.**\n\n**A5:** Thank you for this constructive suggestion. Due to the rebuttal policy, we are not permitted to upload new images. Therefore, we present the OCR results in a formatted table below, which we hope you can reference alongside the figures in our main paper. We used the same multilingual PP-OCR V4 model as in our main experiments. All characters that differ from the Ground Truth (GT) are marked in bold, and each missing character is represented by an underline.\n\n**Figure 3** **OCR** **Results:**\n\n| Methods  | Text Region 1 (Conf) | Text Region 2 (Conf) | Text Region 3 (Conf) |\n| --- | --- | --- | --- |\n| Input    | 等海外高校。, 0.980  | 清华大学（含深研院）, 0.968 | 哈工大（**合**深圳院）, 0.988 |\n| R-ESRGAN | 等海外**离**校。, 0.957  | 清华大学（含**源**研院）, 0.974 | 哈工大（含深圳院）, 0.948 |\n| HAT      | 等海外**商**校。, 0.858  | _华大学（含深研院）, 0.968  | 哈工大（含深圳院）, 0.987 |\n| SeeSR    | ___**人**学, 0.624       | _____**明工火**）, 0.622        | _______                   |\n| SupIR    | _海**并汶**，, 0.546     | _华大_, 0.810               | **防工**大（**不质**）, 0.755     |\n| OSEDiff  | **BBRAD**, 0.756         | **HDAW IAWWR**), 0.648          | **FAIA CRHMAY**, 0.585        |\n| MARCONet | 等海外**峦**校。, 0.867  | **国**华大学（含_**级**院）, 0.874   | 哈工大（含深圳院）, 0.936 |\n| DiffTSR  | **净**外**直**, 0.602        | 清华大学（含深研院）, 0.986 | 哈工大（含深_院）, 0.994  |\n| Ours     | 等海外高校。, 0.987  | 清华大学（含深研院）, 0.982 | 哈工大（含深圳院）, 0.996 |\n| GT       | 等海外高校。, 0.987  | 清华大学（含深研院）, 0.996 | 哈工大（含深圳院）, 0.996 |\n\n**Figure 4** **OCR** **Results:**\n| Methods  | Text Region 1 (Conf) | Text Region 2 (Conf) | Text Region 3 (Conf) | Text Region 4 (Conf) |\n| --- | --- | --- | --- | --- |\n| Input  | enhua, 0.994| 竞舟北路, 0.993 | 丰潭, 0.998  | Fengtan, 0.996  |\n| R-ESRGAN | _nhua, 0.990| 竞舟北路, 0.987 | 丰潭, 0.998  | Fengtan, 0.989  |\n| HAT | enhua, 0.994| 竞舟北路, 0.991 | 丰潭, 0.999 | Fengtan, 0.997  |\n| SeeSR  | _____ | ____  | 丰**派**, 0.994| F**na**g**fer**, 0.813  |\n| SupIR  | **o**nhu**s**, 0.958 | 竞舟北路, 0.853 | 丰潭, 0.999  | \\_**on**___, 0.997 |\n| OSEDiff  | _____ | ____  | 丰**萍**, 0.784| ______ |\n| MARCONet | _____| 竞舟北路, 0.995 | 丰潭, 0.998  | Fengtan, 0.987  |\n| DiffTSR  | _____ | 竞舟北路, 0.996 | 丰_, 1.000  | Fengtan, 0.987  |\n| Ours| enhua, 0.993 | 竞舟北路, 0.995 | 丰潭, 0.999  | Fengtan, 0.996  |\n| GT  | enhua, 0.996  | 竞舟北路, 0.995 | 丰潭, 0.997  | Fengtan, 0.997  |"}]

Author rebuttal:

Meta-review: This paper proposes a text-aware diffusion model and the FTSR dataset for real-world image super-resolution (SR), effectively preserving the structural fidelity of text regions. The submission received 4 positive ratings (2 Accept and 2 Borderline Accept), and all reviewers agreed that the work is both effective and novel.

The AC finds that the paper offers helpful insights into text-aware image restoration, particularly in preserving text structure fidelity, which has been a long-standing challenge for diffusion-based SR methods. The authors are encouraged to incorporate the reviewers’ constructive feedback in the final version, such as clarifying the distinction between “locating” and “retrieving” raised in [Q3] by Reviewer GxHi, and correcting the data pipeline issue highlighted by Reviewer AA8X. The release of the FTSR dataset is particularly encouraged, as it is expected to facilitate further progress in this field. The AC recommends acceptance of this paper.
Decision: Accept (poster)