Text-Aware Image Restoration with Diffusion Models
Authors: Jaewon Min
Jin Hyeon Kim
Paul Hyunbin Cho
Jaeeun Lee
Jihye Park
Park Min Kyu
Sangpil Kim
Hyunhee Park
Seungryong Kim
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Model
Image Restoration
Text-spotting
Scene-Text Image Super Resolution
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We introduce TAIR, a new task for restoring images with text contents by combining diffusion-based restoration and text spotting. We also propose a dataset, SA-Text,  enabling joint optimization of visual and textual fidelity.

Abstract: While diffusion models have achieved remarkable success in natural image restoration, they often fail to faithfully recover textual regions, frequently producing plausible yet incorrect text-like patterns, a phenomenon we term text-image hallucination. To address this limitation, we propose Text-Aware Image Restoration (TAIR), a task requiring simultaneous recovery of visual content and textual fidelity. For this purpose, we introduce SA-Text, a large-scale benchmark of 100K high-quality scene images with dense annotations of diverse and complex text instances. We further present a multi-task diffusion framework, TeReDiff, which leverages internal features of diffusion models to jointly train a text-spotting module with the restoration module. This design allows intermediate text predictions from the text-spotting module to condition the diffusion-based restoration process during denoising, thereby enhancing text recovery. Extensive experiments demonstrate that our approach faithfully restores textual regions, outperforms existing diffusion-based methods, and achieves new state-of-the-art results on TextZoom, an STISR benchmark considered a subtask of TAIR. The code, weights, and dataset will be publicly released.

Published as a conference paper at ICLR 2026
TEXT-AWARE IMAGE RESTORATION WITH DIFFUSION MODELS
Jaewon Min'
Jin Hyeon Kim KAIST AI
Paul Hyunbin Cho KAIST AI
Jaeeun Lee KAIST AI
KAIST AI
Jihye Park Samsung Electronics
Minkyu Park Samsung Electronies
Sangpil Kim Korea University
Hyunhee Park Samsung Electronics
Seungryong Kim f
KAIST AI
LQ Image
Stable
SeeSR
SUPIR
FaithDiff
GMD SENTELE
3X0 OZRTILE
3110 GANTIE
GENTIE
IMD GENTILE
OMO GZNTILE
IMO GENTILE
INIO GANTIDE
IMO CENTILE
DifFBIR
Dvf
TeReDiff (Ours)
OMD
SXNTILE
IMD GATTELL
GENTILE
GENTILE
LQ Image
Stable5R
SeeSR
SUPIR
FaithDiff
MOARITA
BOSSIYA
BOSSIYA
BODWLYA
ROSBIYA
DiffBIR
Di
TeReDiff (Ours)
WHOKMA
pOOC NUSSAAA
NOSSIYA
RDOSSVA
ROSSIYA
ROSSIYA
Figure 1: Text-Aware Image Restoration (TAIR). Given a low-quality (LQ) image containing degraded texts, our method faithfully restores the original textual content with high legibility and fidelity, whereas previous diffusion-baar based methods (Blattmann et al. 2023 Wu et al. 2024 Yu et al.
2024
Chen al. 2024a
Lin et al. 2024) often fail to recover the text regions.
ABSTRACT
While diffusion models have achieved remarkable success in natural image restora- tion, they often fail to faithfully recover textual regions, frequently producing plausible yet incorrect text-like patterns, phenomenon term text- text-image hallu- cination. To address this limitation, we propose Text-Aware Image Restoration (TAIR), task requiring simultaneous recovery of visual content and textual fi- delity. For this purpose, we introduce SA-Text, a large-scale benchmark 100K high-quality scene images with dense annotations of diverse and complex text
:: Equal contribution, 1; Corresponding authors

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Published as a conference paper at ICLR 2026
instances. We further present multi-task diffusion model, TeReDiff, which lever- ages internal features of diffusion models to jointly train a text-spotting module with the restoration module. This design allows intermediate text predictions from the text-spotting module to condition the diffusion-based restoration process during denoising, thereby enhancing text recovery. Extensive experiments demonstrate that our approach faithfully restores textual regions, outperforms existing diffusion- based methods, and achieves new state-of-the-arr results on TexiZoom, an STISR benchmark considered a subtask of TAIR. The code, weights, and dataset will be publicly released.
INTRODUCTION
Recent studies (Yue et al., 2023 Wang et al. 2024 Lin et 2024 Wu al. 2024 Yu al. 2024
Chen et al. 2024a) have demonstrated remarkable capabilities in image restoration by leveraging powerful generative priors of diffusion models (Ho al. 2020 Rombach al., 2022). achieving superior perceptual quality across various degradation scenarios. However, previous models still struggle to recover text regions, as shown in Fig. [ Since these models rely on the powerful generative priors of diffusion models, they often synthesize plausible text-like images rather than reconstructing the exact characters, leading to texi-image hallucination. In other words, most image restoration studies have focused overall perceptual quality without explicitly addressing text readability.
Yet, textual content provides semantic cues that are essential for scenarios such as document digiti- zation (Feng et al. 2023 2025). street sign understanding (Tabernik & Skočaj 2019 Ertler et al.)
2020). or AR navigation Li et al. 2020). where even slight distortions can compound into significant information loss. To this end, scene text image super-resolution (STISR) methods tried improve the perceptual quality and legibility of cropped text regions (Dong et al., 2015 Wang et al. 2019b) Xu at. al. 2017 Ledig et al. 2017), boosting recognition accuracy, yet its patch-level focus introduces fundamental limitations. The global context is discarded by focusing solely on cropped text regions, thereby ignoring information crucial for overall visual coherence.
To address these limitations, we propose new task: Text-Aware Image Restoration (TAIR). Unlike STISR methods (Liu al., 2023a Noguchi et al. 2024 Singh et al. 2024 Ye al.
2025 which
operate on cropped text images under the single word assumption, TAIR restores full scene images
while explicitly preserving text fidelity. contrast to existing image restoration approaches (Wang
et al. [202 Liang et a 2021; Yue et al. [023 Wang et al. 2024 Lin et al., 2024 Wu et al. 2024
Yu et al. 2024] Chen et al. 2024a), TAIR necessitates the integration of textual semantics within the restoration process. While concurrent work (Hu et al. 2025) addresses text-aware restoration through segmentation maps, our approach directly leverages linguistic information via text recognition, enabling explicit OCR-guided restoration. However, primary challenge lies in the absence of suitable datasets.
Existing image restoration benchmarks (Agustsson & Timofte 2017 Cai al., 2019 Li et al. 2023
Wei et al., 2020) are not designed for TAIR, making it difficult to train models that align visual restoration with text readability. While some datasets (Singh al. 202 Veit et al., (016 Gupta
ct al. 2016 Karatzas et al.] 20015) Yuliang et al. 2017: Ch ng & Chan (017) provide image-text pairs for text-spotting, they remain suboptimal due to limited scale and quality. These datasets are
typically generated synthetically (Gupta et al. 2016) or annotated manually (Singh et al. 2021 Veit
al. 2016 Yuliang al. 2017 Ch'ng
Chan 2017), and often suffer from low resolution.
To overcome this, we introduce a large-scale dataset, SA-Text, specifically curated for TAIR. The curation pipeline applies text detection and VLM-based recognition, followed by filtering of low- quality samples to yield high-quality crops with accurate annotations. Built on SA-1B (Kirillov) al. 2023), SA-Text comprises 100K images with diverse fonts, sizes, orientations, and complex visual contexts, providing a robust benchmark for evaluating both perceptual restoration quality and text fidelity. To the best of our knowledge, this is the first benchmark addressing both perceptual restoration quality and text fidelity jointly.
Lastly, we propose Text Restoration Diffusion model (TeReDiff), designed to accomplish TAIR by combining diffusion-bast based image restoration model with a text-spotting module. Inspired by recent studies demonstrating the effectiveness of diffusion features in vision downstream tasks (Liu et al.
2023al Noguchi et al. 2024 (Singh et al. 2024 Ye et al. 2025), directly diffusion U-Net
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Dotection and Patch Cropping
Detection and Box Cropping
VLM Filtering
VINI
MONEY MONEV
MORES
WORKS
VLM Classification
WORKS
VLM topac Phanae
Clard du Inage ini of Eiloetog indi bani daccea
Teat Detection
"Rusd The enee eolu
Model
Recogrttio
maach furfiet "Ladd "Land Sfve
Figure 2: SA-Text curation pipeline. A eextsspotinng module (Huang al. 2024) detects text regions across the image. To recover missed text, the model is reapplied to smaller patches. Two Assionlanguage models (VLMs) (Bai et al. 2025 Lu et al. 2024 then transcribe the text within the detected boxes, and only patches where both agree are kept. Finally, a single VLM (Bai et al. 2025) classifies each patch by sharpness and blurriness.
features input to the text-spotting module. This design allows the restoration model to benefit from semantically rich and text-aware representations during training. Furthermore, at inference time, the output of the text-spotting module can leveraged as input text condition prompts for subsequent denoising steps, thereby jointly enhancing visual quality and text readability.
In summary, our key contributions are as follows:
We introduce Text-Aware Image Restoration (TAIR), which explicitly requires simultane- ous recovery of scene appearance and textual fidelity.
We release SA-Text, a dataset of 100K high-quality images densely annotated with diverse texts, enabling rigorous evaluation and further research on text-conditioned restoration.
We propose TeReDiff, a multi-task diffusion model that forwards diffusion features to a text-spotting module during training and uses the spotted text as a prompt inference, enhancing both perceptual quality and legibility.
RELATED WORK
Diffusion-based image restoration. Advances in diffusion models for generating high-quality
images (Ramesh et al.) 2021 Rombach et al. [002)] Podell et al., 2023) have extended their use to
image restoration (IR) (Lin et al. [014||MeeM et al. 2024]] Sahak et al. [023] Saharia et al.) 2022).
Unlike GAN-based IR methods (Wang et al.) 2021 2018 Zhang et al. 2021 Che al. 2016
Mao et al. 2019), which suffer from unstable training and mode collapse, diffusion-based IR offers stable training, robustness, and improved generalization through iterative denoising. SR3 (Saharia et al., 2022) first applied diffusion models to IR, achieving state-of oftthcartt results on facial and
natural image datasets. Subsequent works (Sahak et al. 2023 Wang al. 2024) further advanced diffusion-basel IR by addressing degradation and fidelity challenges.
Text-spotting Scene text-spotting is the joint task of detecting and recognizing text in natural images. Early methods treated it as two separate tasks: text detection, which localizes regions using region- or iegmentation-baseen techniques (Liao et al.] [017]] [iu & Jin [017]] Wang et al.) 2019a [iao et al.] 2020). and text recognition, which interprets the localized content via sequence modeling (Graves et al. 2006 Shi et al.) 2016 2018). Recent detection approaches employ poly- gon (Ye et al. 2023a) or Bezier curve representations (Liu et al.) 2020) to improve localization, while transformer- baseed architectures inspired by DETR (Carion al. 2020) have further enhanced detection performance (Ye et al. 2023a Tang et al. 2022) Zhang et al., 2022). Recognition has concurrently been formulated
image-to-fext translation task, with advances in visual feature
extraction and language modeling
Yuu et al. 2020 Fang et al. 2021 boosting accuracy.
Scene text image super resolution. Scene text image super-resolution (STISR) aims to enhance the quality and legibility of word-level cropped images. Early approaches employed CNNs (Dong
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et al. 2015) and GANs with text-aware losses Wang al. 2019b: Xu et al. 2017 Ledig al.
2017
followed by residual-block architectures (Mou et al. 2020) and high-frequent preservation
methods (Wang et al., 2020). More recent ransformer-basee models incorporated text priors (Ma et al.
2023 2022). mu multimodal cues (Zhao et al. 2022), and position- or content-aware losses (Chen
al.
2022). Recently, diffusion-based approaches have also been explored for STISR (Liu et al.
2023a Noguchi et al. 2024 Singh et al. 2024 Ye ct al. 2025).
3
SA-TEXT DATASET
In this section, present SA-Text curation pipeline, which comprises detection stage and recognition stage, as described in Sec. .3.1 As fully automated pipeline, it can be easily scaled up larger datasets with minimal user intervention. We then analyze the resulting dataset, SA-Text, to demonstrate its suitability for TAIR in Sec. 3.2 The accuracy of this automated pipeline was validated through human verification, with additional details provided in Appendix A Note that although our discussion focuses on English for clarity, the pipeline itself is aggusic-agnostic, with an example on Chinese provided Appendix A
3.1 DATA CURATION PIPELINE
TAIR requires datasets where text instances are paired with bounding boxes. TextOCR (Singh et al. 2021) provides dense annotations but at low resolution, making it unsuitable for restoration.
TextZoom (Wang et al., 2020), built on RealSR (Cai et al., 2019) and SR-RAW (Zhang et al. 2019),
offers high-resolution annotations but is limited in scale, with manual construction further hindering scalability. Existing image restoration datasets (Li et al. 2023; Karatzas et al. 2015 Lim et al. 2017) contain high-quality images but lack text annotations, while text-spotting datasets provide dense labels yet mostly low-quality images, leaving both inadequate for TAIR. To overcome this gap, we propose scalable curation pipeline that produces high-resolution images with dense text annotations, tailored for TAIR. We source images from SA-1B (Kirillov et al. 2023). corpus of 11M high-resolution images, ensure quality suitable for restoration.
Text detection and region cropping. The first stage of our pipeline applies a dedicated text detection model high-resolution images identify text regions. As shown in Fig. 2 small instances are often missed at this resolution, mixed with other incorrect detections. To mitigate these errors, based on the initial detections, we extract 512x512 crops that fully enclose at least one complete instance and ensure no instance appears in multiple crops. We then re-run the detector on each crop, where the reduced field of view improves recall and refines the incorrect detections, with only minor false positives which are subsequently removed in the VLM-based filtering stage.
VLM- text recognition and filtering. Recent vasion-language models (VLMs) (aai et al.
2025 Lu et al. 2024) show strong text-recognition performance OCRBench (Liu et al. 2023c), validating their use as recognition backbones. To leverage their accuracy without suffering from localization shortcomings (Ranasinghe et al.] (024), we isolate each text instance via detector polygons and input the cropped patches to two VLMMs (Qwen2.5-VI. (Bai et al. 2025) and OVIS2 (Lu retained, filtering out misreadings, hard-to-read texts, and false positives. We then apply an additional filtering stage with Qwen2.5-
al. 2024)). Only instances with identical transcriptions
VL (L et 2025) to remove images with intentional blurring of human faces, license plates, and privacy-sensitig regions, which global blur metrics such as Laplacian filtering often miss. This dual- VLM verification and filtering pipeline enhances the quality of the dataset for TAIR.
Task Datuset
Text-Spotting
Image Restoration
TAIR
COCOText
ICDAR2015
CTW
TetalText
TextCR
LSDIR
DIV2K
Flickr2K
SA-Te Textext (Ours)
HQ Text
X
x
/
# of Img
13,880
1,000
1,000
1,255
21,778
84,991
800
2,650
105,330
Table 1: Comparison with other datasets.
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(a) Detailed Architecture
(b) Training Pipeline
imagg
Ctrl-Net
Diffusion Loss
Text Spotting Loss Lest Eiee
CommokMett 6
Eype
Decoder
U-Net
Recogetiin
(c) Pipalino
Lo Iagg
Ctrl-Net
Detection Decode yoa
HQ Image
Dilluioo Fasture
Mullicca scale fealero
Detective
Feb
U-Net
Diffuston Feature Encoder
Recogritian
Recog got
*TWrenggs
Figure 3: Overview of the TeReDiff architecture, training, and inference pipeline. TeReDiff incorporates a text-spotting module into a diffusion- image restoration framework, using text supervision during training and recognized text as input prompts during inference enhance text- aware image restoration.
3.2 DATASET ANALYSIS
Leveraging our dataset curation pipeline, specifically designed to improve text annotation accuracy,
construct SA-Text 100K from SA-1B (Kirillov al. 2023). The pipeline is fully automated,
making it readily scalable for curating even larger datasets. We compare ---Text with datasets for
text-spotting (Veit et al., [0166] Karatzas al. 2015 Yuliang al.
2017
Ch ng & Chan 2017 Singh
et al. 2021) and image restoration (Li et al. 2023 Karatzas et al. 2015 Lim et al. 2017) 7)... As shown
Tab. [ SA-Text the only one that provides both high-quality images and explicit text annotations,
while also containing the largest number of images among all compared datasets. Since it is curated
from the scene-level SA- AB dataset, SA-Texi contains diverse text styles and visual contexts.
TEREDIFF MODEL
The overall architecture of TeReDiff with its training and inference pipelines is illustrated in Fig. A diffusion-based image restoration module enhances the visual and textual fidelity of degraded inputs, while text-spotting module predicts polygons and transcriptions that formatted into prompts to guide subsequent denoising steps. These two modules are jointly trained through shared diffusion features, enabling effective interaction and unified optimization of image restoration and text-spotting.
4.1 ARCHITECTURE OVERVIEW
Diffusion-basser image restoration module. Given low-quality (LQ) image Iy. the goal is to recover high-quality (HQ) image Ing with enhanced visual and textual fidelity. To achieve this, we build on DiffBIR (Lin al. 2024). comprising U-Net li and ControlNet (Zhang et al.. 2023) as the baseline for the image restoration module. DiffBIR employs lightweight degradation removal module and VAE (Kingma al. 2013) produce conditioning latent Formally, the HQ image Ing encoded into latent 2o. which undergoes forward diffusion process that progressively adds noise, yielding a noisy latent o at timestep t. The conditioning latent e then concatenated with z to form c; concat(2, ee, c), which, with text prompts Pt, is used to condition the U-Net 2/ through the ControlNet C.
Text-spotting module. Incorporating a text-spotting module within the restoration framework enables the diffusion model to learn text-aware features through gradient supervision. Unlike conventional methods relying on ResNet features (Liu et al., 2020 Yu et al. 2020 Kittenplon et al. 2022 Ye et al. 2023b; Zhang et al. (202). our module directly exploits semantically rich diffusion features. Specifically, after a single forward pass. we extract intermediate features from the four decoder blocks of U-Net 2/ (Xu et al. 2023 Tang et al. 2023
Luo et al. 2024). The features from
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LG
T-999
T-652
T
HQIGT)
WUTER
NUTION
Recognized
box
"LOUSS VUITTON" "LOUIS VUITTON"
"LOUIS VUITTON"
Input for neat timestep
Figure 4: Textual prompt guidance. Intermediate text predictions from the text-spotting module used to condition the restoration module at each denoising step. As shown, the character "S' is corrected to 'T' during the denoising process, demonstrating that detected boxes and recognized text can be refined the image is progressively restored.
each decoder block are projected by convolutional layer match channel dimensions, and the resulting features are stacked into a multi-scale feature input F E RLDDD where is the number of tokens and D the transformer hidden dimension. This multi-scale input enhances text-spotting while simultaneously benefiting restoration through shared representations. A transformer encoder & and two decoders, Delet and Dre then decode polygon-charane character tuples Y {(d.)) .T r())) where af" are polygon control points, ((9) the recognized characters, and K the number of instances above a confidence threshold T. Further analysis of leveraging diffusion features in the text-spotting module provided in Appendix
4.2 TRAINING
Our training strategy is divided into three sequential stages, each focusing different components of the framework. In summary, Stage trains the diffusion-based image restoration module, Stage 2 trains the text- eext-spotting module, and Stage 3 jointly optimizes both for mutual benefits. Detailed formulations of the loss functions are provided in Appendix C
this stage, we focus on training the image restoration module. U-Net 24 and ControlNet C are optimized to learn to remove noise from degraded images, guided by text prompts describing the texts, while the text-spotting module remains frozen. Given the diffusion timestep t, prompt Pt. and control input c, the model learns noise prediction network ep that estimates the noise added to the HQ latent 2 with diffusion loss.
Stage 1.
Stage 2. During this stage, we train only the text-spotting module using the conventional loss formulation adopted in transformer-basee text-spotting methods (Huang et al. 2024 Zhang et al.) 2022 Qiao et al. 2024). The module outputs polygonal regions and character sequences, with bipartite matching (Carion al. 2020) applied associate predictions with ground truth. The encoder branch is optimized with classification and localization objectives, while the decoder branches refine polygon boundaries and character transcriptions. This stage preserves the standard training paradigm for text spotting while adapting it to diffusion features. The detailed optimization objectives of the text-spotting module are provided in Appendix
Stage 3. In the final stage, both the diffusion- based restoration module and the text-spotting module are optimized jointly. This unified training allows restoration to benefit from text-aware supervision and, simultaneously, improves text-spotting performance through cleaner image features. The overall objective combines the restoration loss with text detection and recognition losses, balanced by weighting factor encourage mutual gains across tasks.
4.3 INFERENCE
Textual prompt guidance. At each denoising timestep, the text-spotting module produces text detection and recognition outputs in the form of polygon-character tuples. To further guide the restoration of accurate textual content the LQ image, the intermediate predicted characters are employed as text conditions for the subsequent timestep. Specifically, at timestep f. 1, the characters

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Published as a conference paper at ICLR 2026
ABCNet 2
20211
TESTR Zhann all
Level
Model
Dosection
Endan-Ead
Dotection
Endan-Eal
Phocision:11
Recili()
F1-Scoro(T)
Nowet))
FulkT)
Prerivion(1)
Resllki
F1-Scoeo(T)
Necett))
Fulk())
(QT)
92.16
86.85
89.43
71.29
81.05
90.59
87.81
75.90
(Lv1)
29:51
04.42
24.29
10.24
25.03
Real ESRGAN Wing 20211
$3,79
45.34
21.45
65.19
41.9%
56.24
22.40
SwittR
54.55
40.93
22.00
57.93
3962
54.63
25.50
33.75
KesShif
$1.95
40.07
29.40
28.14
18.47
15.81
50.96
12.99
Levell
DiNBIR [1ng--e Puree
StaleeR
76,29
77.90
55.44
36.44
64.78
64,58
23.14
21.33
32.73
29.29
64.44
$4,00
52.13
50.68
64.34
63.34
24.12
25.51
31.84
35.47
DinRRRR LiLuRRB 2310
59.01
$1.85
$2.43
15.26
20.71
00.55
$1.99
55.04
16.74
22.87
SccSR
Wasssin 50133
70.00
61.88
65.60
20. 16
25.63
78.55
55.76
65.32
23.51
$2.83
SUPIR
TUAA 2uN43
43.64
49 49.45
06.37
14.58
19.34
53,02
46.19
49.37
17:44
22.20
Portthrr
09.16
01.51
60.12
201.4
27:75
78.30
57.12
06.23
22.50
31.99
TeReSMIf (Ours)
58.34
26.59
56.90
07.47
26:19
LQ (LYv2)
$7.67
22:89
36.30
20.49
27.52
78.45
23.95
36.04
20.49
27.37
Real ESRGAN Wans et.l. 1021
41.12
54.64
18.31
38.80
19.29
Lisss Fures
37.31
17.82
19.07
RoShil
25.54
12.05
Levi2
Di0BIR
Suall:5R
72.06
53.94
51.95
19.55
19.90
48.87
00.50
21.54
20.006
27.06
Diffiik
53.28
51.18
14.25
50.18
15.15
21.43
SedSR
Wir Prins
68.95
6065
64.53
19.48
26.72
77.50
54.49
03.99
21.83
29:17
SUP
1i Vine
43.01
45.65
41.35
13.21
13.47
$3.54
4522
47.84
15.50
FuraDir
Chicne
20240
66.62
59.34
62.77
15.94
25.99
76.16
54.17
63.31
20.98
28.56
TeRebil (Ours)
$3.02
56.30
67.10
24.42
33.23
86.95
$2.86
65.75
26.39
35.13
1.0 (Lvx3)
85.36
13:24
22.92
12.17
16.95
76.01
15.37
25.57
12.52
17.72
Red-ESRGAN Wayre m [000
72.48
28.65
43.07
11.89
16.37
76.51
27.02
30.00
12:13
18.23
SwisdR
74.41
25.57
38.06
11.27
16.33
78.38
24.16
36.04
11.85
17.12
RecShif
11 RJ7i)
75.00
22.57
34.11
10.80
15.19
K1.10
20.04
$3.113
9.89
15.03
Level
Saatesie 1ea
DIARIR
6763
36 03
49.31
08.77
13.83
19.90
18.91
72.31
15:22
50.65
15.01
13.65
19.05
23.62
DIMBIRT [L.1 21
40.62
42.31
10.15
41.25
45.09
10.75
15.41
Wi
55.06
46.33
13.38
43993
14.93
36.35
11:21
47.91
13.69
45.01
TeRollif (Ours)
$1.76
46.11
$7,30
19.61
27.50
$4.50
42.02
56,13
19.92
28.34
Table 2: Quantitative results of TAIR on SA-Text. Each block shows the performance of various image restoration methods under different degradation strengths, evaluated using two text-spotting models. 'None' refers to recognition without lexicon, and "Full' denotes recognition with full lexicon. Best results are in bold and second-best are underlined. DifBIR (Lin et al. 2024)t (denoted as v2.1 in Fig. uses GitHub-released weights that are further trained from those in the original paper.
predicted by the recognition branch of the text-spotting module at the previous timestep t, fr. 1 used as the text prompt Pi+1- An example of textual prompt guidance is illustrated in Fig.
5
EXPERIMENTS
5.1
EXPERIMENTAL SETTINGS
Training and evaluation dataset. We train our TeReDiff on SA-Text, which contains 100K high-
quality 512x 512 images. Synthetic degradations are applied using the Real-ESRGAN pipeline (Wang
et al. 2021). a method widely adopted in image restoration studies (Yue et al. 2023 Wang et al.
2024 Lin et al. 2024 Wu al. 2024; Yu al. 2024). For testing, we curate a separate IK subset of
Text. Similar a to DASR (Liang et al.] 2023) and FaithDiff (Chen et al.) 2024a), we apply three
progressively challenging degradation levels to our test set, to evaluate the model in various difficulty settings. We further construct Real-Text, a set of real-world HR-LR pairs extracted from RealSR (Cai al. 2019) and DRealSR Wei et al. (020)) with text annotations using our dataset curation pipeline. Lastly, we evaluate Teff on TexiZoom (Wang et al.) 2020). an STISR benchmark consisting of
paired low-resolution (LR) and high-resolution (HR) text images sourced from real-worke images.
Evaluation metrics. We evaluate text restoration using pretrained text-spotting models (Liu et al. 2021; Zhang et al.) (022). reporting Precision, Recall, and Fl-score for detection and F1 Fi-score for end-to-end recognition. The evaluation operates in two stages: (1) text detection assessed using Precision, Recall, and F1-score to measure how well text regions are localized, and (2) for detected text regions, End-to-End metrics evaluate recognition accuracy by comparing transcribed text against ground-truth annotations using exact string matching. This process directly measures linguistic correctness. If restoration produces inaccurate or garbled text, End-to-End Fl-scores will be lower. These metrics collectively measure functional legibility, where restoration is successful when words are more readily detected and recognized. This evaluation protocol follows standard practice in text-spotting benchmarks. Image restoration is assessed with reference metrics, using PSNR and
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ABCNet
TISTR (Zhang
Model
End-toFad
End-so-Fr
Precissinn())
Recall(t)
Fl-Scone(?)
None(t>
Fullc1)
Precision()1
Recall(t>
Fl-Seone())
None(1)
FulkK]>
(GT)
90.23
87.72
72.06
79:48
90.29
85.77
87.97
74.50)
1.Q
89
59.77
64.32
47.08
55.11
Real-FSRGINN Wane
2021
42.
SwinIR [Lann caal p04
80.29
87.45
59.64
38.
42.63
$2.92
47.89
60.72
39.97
44.56
ResSain
23 20r3
81.17
35.76
47.09
30.95
34.87
$2.25
39.91
53.74
33.31
39.99
StobleR
79.79
59.80
68:42
41.23
47.64
82.1 $2.19
60.30
69.82
42.
49. 3.
DiJBBR
66.04
59.09
02.71
33.75
40.05
76.33
61,87
68.35
39.27
46.11
DeMBIR
LLn A114
55.31
50,00
55.07
26, 85
31.25
58.99
60.19
59.55
31.41
35.96
SeeSR
[W 0 2014
68.12
63,46
65.71
37.
43.43
74.29
62.47
67.87
40.34
46.54
SUR
44.00
42:21
48.39
30.59
20140
47.97
ToRahiT
R3,85
74.88
55.01
56.45
Table 3: Quantitative results TAIR on Real-Texx Using text-spotting models, we evaluate the
accuracy of text detection and recognition of the restored images on Real-Text
Dataset
Model
PSNR(1)
SSIM())
LPIPS(4)
DISTS(
FID(J)
NIQE(L)
MANIQA(1)
MUSIQ(1)
CLIPIQA(()
DiMBBR
19.58
0.4965
0.3636
0.2080
45.10
5.107
0.6771
73.33
0.658y
SA-Text
DiABIRT
16.81
0.4638
0.4001
0.2219
47.28
5.449
0.6890
72:55
0.6345
TeReDiff (Ours)
0.5717
0.2828
0.1702
0.6471
0.6145
DiABIR
23.00
0.6516
0.4108
0.2925
87.46
7.054
0.6147
66.84
0.5679
Real-Tor Text
DiABIR
19.11
0,5107
0.5233
0.3127
80.59
8.42
0.5873
61.46
0.1961
TeReDiff (Ours)
23.37
0.7849
0.2848
0.2386
68.94
7.643
0,5637
62.02
0.4545
Table 4: Quantitative results of image restoration. We evaluate the image quality of our TeReDiff compared with its baseline. The degradation pipeline (Wang al.) (02T) used in prior works is applied. DiffBIR (Lin et al. ( (denoted as v2.1 in Fig. [] uses GitHub-released weights that are further trained from those the original paper.
SSIM (Wang et al. 2004) for fidelity, LPIPS (Zhang et al. 2018) and DISTS (Ding et al. 2020) for perceptual quality, and FID (Heusel al. (017)) for distributional similarity, as well as coffreference metrics including NIQE (Zhang et al. 2013). MANIQA (Yang et al. 2022). MUSIQ (Ke et al. 2021
and CLIPIQA (Wang et al. 2023
Implementation details. The image restoration module is initialized from DiffBIR (Lin et al. 2024), and the text-spotting module from TESTR (Zhang et al. 2022). Training is performed using the A optimizer with default parameters. The learning rate is set to x 10 for Stage 1 and Stage 2, and 10 10-5 for Stage 3. Publicly available checkpoints are used for all models. The input LR and output HR images are both 512 x 512 in size. Further details can be found in Appendix
5.2 MAIN RESULTS
Quantitative comparisons. Tab. Tab..[shon shows detection and recognition metrics on SA-Text. TeReDiff achieves the best Fl-score at every level on both text-spotting models. Prior models often lose recognition accuracy at Level2 and Level3, dropping below the raw low-resolution inputs because stronger degradations intensify text-image hallucination. In contrast, TAIR consistently restores tex- tual regions, preserving recognition performance even under the strongest degradations. Furthermore, Tab. 3 fillustrates the performance of TeReDiff in real-world scenarios. For image quality, Tab, 4 demonstrates that our model outperforms the baseline on reference-based metrics while achieving comparable performance on non-reference metrics. These results confirm that incorporating text- focused restoration objectives does not compromise overall image quality. Although existing image quality metrics are not well-suited to capturing the text-image hallucination problems that we aim to address, our text-aware approach maintains competitive image quality while significantly improving text legibility, validating the primary focus of this paper. Additional quantitative comparisons of image quality with other methods are provided in Appendix
Qualitative comparisons. Fig. shows representative results on SA-Text test set. Previous restoration methods often produce blurry characters and inconsistent stroke widths, which are text- image hallucinations under severe degradations due to reliance the generative priors of diffusion models. In contrast, TeReDiff consistently restores readable text in challenging regions. These qualitative improvements align closely with the quantitative results in Tab. 3 highlighting model's
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Published as a conference paper at ICLR 2026
Method
CRNN (Shi et all. 2016
MORAN Lue ct Daall22011
2019
ASTER Shi ct al. 2018
Easy
Medium
Hard
Avg.
Easy
Medium
Hard
Avg.
Easy
Medium
Hard
Ang.
(Bicubic)
11.1%
26.8%
60.6%
37.9%
30.8%
61.7%
42-19 87.7%
31.2%
47.2%
HR
76.45%
75.1%
64.6%
72.4%
91.25%
85.36
74.2%
84.1%
94.2%
76.25
86.6%
Texiii st al
20
64.8%%
55. 4%
39.95
54.2%
77.7%
62.5%
44.6%
62.7%
8008%
66. 66.5%8
48.7%
66,4%
TCDM
Nogacht
55.7%
77.6%
62.9%
15.9%
81.3%
65.1%
50.1%
DCDM
Simee clai 2049
65.7%
57.3%
41.4%
55.51
78:
63.5%
45.3%
63.118
81.8%
65.1%
47.47
65.8%
TextSR
Te & M 2023
69.9%
60.0%%
43.9 13.9%
19.7%%
78. 796
63.2%%
47.1%
64.0%
81 81.0%%
64.,5%
48.7%
65.8%
TeReDiff (Ours)
75.7%
75.6%
65.8%
72.4%
92.2%
86.0%
76.8%
85.0%
89.67
84,0%
73.0%
82.2%
Table 5: Quantitative results on TextZoom dataset. We evaluate performance using standard text
recognition models (Shi et al. 2016 Luo et al. 2019 Shi et al. 2018) under the common STISR scheme, noting that TextSR Ye et al.
2025) is concurrent work.
TESTR
Zhura 1022
Model
Text Conditiou
Detectus
End-ac-Bie End
TESTR ehargeral.po 2832
Raail())
P1-Scors11)
Deaccioo
Esdio Eaa
Voll
K1 77
47:37
59.99
21.24
70
Precaizel')
F1-Sowe:1)
Suyel
Captozer (lkan)
82.01
40.52
61.99
24.76
31.70
The Thoripinnthhss
$3.35
88.25
09.33
21.6
Grouzd-avve
85.09
61.56
71.44
Suge3
Captooeer (Durs)
Sul
89.47
N6.95
16.21
$3.56
63.50
65.75
26.34
23.46
15.15
11.72
BE.TR
13.35
1D1
Gound-ouuhh
06.18
61.00
71.85
3331
1300
(b) Prompt style. The descriptive-style prompting
(a) Multi-stage training and text conditioning. Stage- shows better performance than the tag-style prompting. wise training and text prompting of the restoration mod- ule improve text restoration.
Table 6: Ablation study on SA-Text (Level2 degradation). (a) Extending training to Stage enables the model to learn text-aware features, thereby improving text restoration performance. Moreover, text conditioning using captioner prompts yields further gains compared to null prompts. (b) The prompt style employed in TeReDiff impacts text restoration performance. In particular, descriptive prompts result in greater improvements than tag-style prompts.
effectiveness in enhancing text clarity without compromising overall image quality. Additional qualitative results are provided in Appendix F
Comparison to STISR methods. As shown in Tab.
method establishes a SOTA on
existing STISR methods (Liu et al. 2023a] Noguchi et al. 2024 Singh et al. 2024 Ye et al. 2025). highlighting the effectiveness of training task formulation. TAIR, is hierarchically broader and more challenging than STISR: while STISR assumes small inputs (c.g.. 64 16) with a single word, TAIR processes full-scene images (c.g.. 512 x 512) containing multiple texts with diverse styles and complex backgrounds. Since STISR benchmarks rely on recognition models for cropped word images, evaluation restricted to a narrow sub-case of our task. Nevertheless, our strong results under this constrained protocol demonstrate the generality and robustness of our framework, clarifying its distinction from existing STISR methods. Qualitative results on TextZoom (Wang et al. 2020 of TeReDiff is provided in Appendix F
full-scene images with diverse context. Importantly, our
5.3 ABLATION STUDY
We conduct ablation studies on SA-Text test set applied with Level2 degradation to evaluate the effectiveness of multi-stage training and the importance of text prompting. Additional ablations on all levels of SA-Text test set and on Real-Text are provided in Appendix
Multi-stage training. To assess the effectiveness of multi-stage training for text-aware restoration, compare the image restoration module trained up to Stage and Stage 3. Advancing training to Stage 3 consistently improves text restoration performance across all text conditions (Null, Captioner, and Ground-truth), the image restoration module has learned text-aware features by receiving gradients from the text-spotting module, as shown in Tab. 6a| The Null, Captioner, and Ground-Truth settings correspond, respectively, to conditioning the restoration module with no prompt. text prompt obtained from captioner (LLaVA (Liu et al. 2023b) or our text-spotting module), and ground-truth text.
Text prompting. As shown in Tab. 6a conditioning textual content extracted from the LQ image consistently improves restoration quality both Stage and Stage 3, highlighting the effectiveness of text prompting for TAIR. In this setting, Stage leverages external captioning module (Liu et al.
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Published as a conference paper at ICLR 2026
2023b due to the absence of trained text-spotting module, whereas Stage employs trained text-spotting module. Complementary results in Tab. (bbpprseent the text restoration performance of TeReDiff under two different prompt styles. Descriptive prompts yield greater improvements than tag-style prompts when conditioning the diffusion-basee ground-truth texts. For example, given the predicted texts "text1," "text2," and "text3," the descriptive- style adopts the format "A realistic scene where the texts textl, tex12, appear clearly on signs, boards, buildings, other objects," whereas the tag-style uses the simpler format "textl, text2....
restoration module with either predicted or
The same trend is observed when ground-truth texts are used as prompts.
CONCLUSION
We revisit image restoration with a new focus: Text-Aware Image Restoration (TAIR), which targets the recovery of textual content in degraded images. This area remains largely unexplored due to the absence of large-scale, annotated datasets. To address this gap, we introduce SA-Text, a curated dataset that uses VLMs to provide automated supervision for text restoration. By leveraging diffusion features in a joint optimization framework, TeReDiff achieves substantial improvements over existing methods and establishes new state-of-the-art performance the STISR task. We hope that TAIR and SA-Text will inspire further research combining text understanding and image restoration.
ACKNOWLEDGMENTS
This research supported by Institute of Information & communications Technology Planning Evaluation (IITP) grant funded by the Korea government (MSIT) (RS-2011 2SS2009--11100055 RS-2024- 00509279, RS-2025-112 2068, RS-2023-00227592, RS-2025-022144799 RS-2024-00457882, RS- 2025-2544 1834, RS-2025-22 22222222221823355 25441838, RS-2025-022 1455592224449 RS-2025-02217255) and the Culture,
Sports, and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture, Sports and Tourism (RS- RRS-2024-00345025, RS-2024-00333066, RS-2023- 00222280, -0--2033000266500), and National Research Foundation of Korea (RS-2024-00346559))
REPRODUCIBILITY STATEMENT
Training details are provided in Sec 5.1 and Appendix D codes, and the model weights to ensure reproducibility.
We will release the proposed dataset, the
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APPENDIX
This appendix is organized follows. Sec. A.c.AAAetaoe details the SA-Text curation pipeline, with emphasis on handling false negatives and incorrect detections. Sec. B demonstrates the benefits of leveraging diffusion features for training the text-spotting module. Further descriptions of the loss functions given in Sec. C Sec. outlines the implementation of the TAIR framework, including evaluation metrics, model architecture, and training details. SerpPorssorrs extended quantitative results, including comparisons with baselines across three levels of degradation on SA-Text and on Real- Text, which captures real-world degradation scenarios. We also present the results of user study assessing human preference for restoration quality. Finally, Sec. focovides qualitative comparisons of TAIR against conventional image restoration and STISR methods, and Sec. dec.([proviiess additional discussions on TeReDiff. Lastly, Sec. H describes the use of LLMs.
A SA-TEXT CURATION PIPELINE
DOMMAYER ER
POLICE
Figure 5: Example images from our SA-Text. Our dataset comprises high- high-quality, diverse images featuring text in varied sizes, styles, and layouts, including curved, rotated, and complex forms, providing robust foundation for the proposed TAIR task.
A.1 PIPELINE DETAILS
We apply our dataset curation pipeline to a subset of the SA-1B (Kirillov et al., 2023 dataset, large-scale dataset originally designed for segmentation tasks. SA-1B (Kirillov et al., 2023) consists of I1M high-resolution images (3, 300 4, 950 pixels on average) that have been downsampled so that their shortest side is 1, 500 pixels. This meets our requirements for high-quality images with sufficient resolution, suitable for the image restoration task. After processing with our dataset curation pipeline subset of SA- IB (Kirillov et al., (023) (18.6% of the dataset), we are able to curate 100K high-quality crops that are densely annotated with text instances, enabling scalable dataset creation specifically for TAIR. Examples of the constructed SA-Text dataset shown in Fig. With its lightweight, modular design, the pipeline can be readily extended to other high-quality, large-scale datasets supply additional data when required.
Trus Posltive
Incorrect Detection
Tabe Negative (Missed Instanca)
Cropping
WORKS
Wist bayr
LEARN WORK
frnnnoa Peety Res
(a) Original Image
(b) Ambiguous Instances
(c) Incorrect Detections
(d) False Negatives
Figure 6: Illustration of our dataset curation pipeline's effectiveness. (a) Original nigh-resolution image with multiple text instances. (b) Ambiguous text instances are removed during the Vision- Language Model (VLM) filtering stage when the two VLMs produce differing recognition outputs. (c) Incorrect detections from the full image are corrected by re-running the detection model on smaller crops; here, the phrase "Powered by Yorkshire Bank" is successfully split into individual words. (d) False negatives (missed instances) from the initial detection the entire image are effectively captured during the second detection pass on the smaller crops; the previously missed instance "WORK" is now correctly detected.
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ABPIB
PARK
KYOTO
Level 1
Sign Up
MADUBARU GO GREEN
Level 2
TIRE SONCH
Level 3
FOOTAAL CLUB
Figure 7: Examples of images classified by blurriness. Images are categorized into Levels 1-3: Level (very blurry), Level 2 (slightly blurry), and Level 3 (clearly focused). Images classified as Level and Level 2 are excluded from the final dataset to ensure that only clearly focused (Level 3) images are used for TAIR.
For the dedicated detection model used in the patch and box cropping stages, we utilize a state-of-the -he art text-spotting model, DG-Bridge Spotter (Huang et al. 2024). As shown in Fig. G, text instances full images
initially missed by the detection model (false negatives) when the model is applied to smaller crops. We observe that running the detection model on smaller crops occasionally results in false positives, defined as detections of text in areas without actual text. Nonetheless, these instances are reliably filtered out in the subsequent VLM filtering stage, as at least one of the VLMs consistently identifies regions without text. Despite the strong recognition capabilities of DG-Bridge Spotter (Huang et al., 2024), we opt for VLMs due to their consistently superior recognition accuracy.
subsequently captured
A.2 VLM FILTERING DETAILS
For filtering out out- and intentionally blurred images, employ a VLM (Bai al. 2025 to classify each image's ' blurriness. We find that prompting the VLM to perform multiclass classification (as illustrated in Fig. 2 offers improved granularity and stricter filtering compared to using simpler binary classification prompt ("blurry" vs. "not blurry"). Examples are shown in Fig. 7 After classification, crops labeled Levels and 2 (very blurry and slightly blurry, respectively) are filtered out from the final dataset. This VLM-based blur filtering ensures that only high-quality, sharply focused crops are used for TAIR.
A.3 HUMAN VERIFICATION
Method
Clean
Blurry
Intentionally Blurred
Qwen-2.5- (Ours)
71%
88%
100% %
InternVL.2.5441
80%
61%
90%
Laplacian variance (th=2000)
50%
88%
88%
Table 7: Performance comparison of methods for blurry image filtering
Our decision to use Qwen2.5-VL. (Bai et al. 2025) for blur-filtering is based internal study we ran to evaluate the performance of different methods for assessing image blurriness. We compared the conventional Laplacian- variiacce bascline method against several VLMs and prompt formulations. The results are shown in Tab. [ where the values represent the classification accuracy. For brevity, values for only the best-performing prompt are listed. We first constructed three balanced sets of
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100 images each corresponding to "clean", "naturally blurry", and "intentionally blurred". When prompted with a short prompt that asks the VLM to classify sharpness on a 3-level scale, as in Fig. 2 Qwen2.5-VL achieved the scores above, exceeding both a classical Laplacian filter and Intern (Chen et al. 2024b), in the two categories most critical for image restoration: detecting blur and privacy-related intentional masking. These results indicate that Qwen2.5-VI is sufficient for blur filtering even without any additional task-specific fine-tuning for image quality assessment.
Finally, to verify the robustness of our pipeline, we conducted a human audit. We randomly sampled 400 training images encompassing 911 text boxes and had coauthors annotate ground-trutt labels. We subsequently compared these annotations against the outputs of our dataset curation pipeline, where we found only two instances where the recognition annotation differed from the ground truth (0.22% label noise). The only remaining issues stemmed from the detector missing tiny or heavily occluded text. This noise level is negligible for TAIR, and we consider the annotations sufficiently reliable for training and evaluation.
A.4 FURTHER ANALYSIS
We further analyze SA-Text using RAM++ (Huang et al. 2023), which assigned tag categories to each image. This revealed 3, 169 unique tags across the dataset as shown in Fig. 8 When categorized into 10 categories based on tags, the "Objects" category showed the largest proportion with over 466K instances, reflecting the prevalence of everyday objects. At the same time, other categories are also well represented, contributing to the diversity and balance of semantic domains covered in our dataset. This highlights the domain richness of our dataset, allowing it to capture both common and specialized concepts, and thereby supporting stronger generalization.
Rernner Catogeries
JANSORA
OROGCES
Guadeer (10000) 1750
Figure 8: Distribution of SA-Text.
A.5 EXTENSION TO MULTI-LINGUAL SETTINGS
2
HBPS
9
ス
8
A
Figure 9: Text annotation results on Chinese using our pipeline.
Due to its modular and automated design, our dataset curation pipeline is inherently language- agnostic and can be easily adapted to multi-lingual settings. The language of detected text during the detection and cropping stages is determined by the text-spotting model. Although we used DG-Bridge Spotter (Huang et al.) (024). an English-focused model, for SA- replacing it with a multilingual text spotter enables detection in other languages. The VLM recognition stage operates similarly, provided the models support the target language. As shown in Fig. 9 we apply the pipeline to Chinese text by substituting PP-OCRv5 (Cui et al. 2025) as the text spotter, while retaining Qwen2.5-VL (Bai et al. 2025) and OVIS2 (Lu et al. 2024) for recognition due to their strong Chinese language capability. To assess annotation quality, we conducted human verification on 100 images with Chinese annotations generated under this setup and observed quality comparable to SA-Text, with only one occlusion-relee related error. Ultimately, annotation quality depends the underlying text-spotting and VLM components, and extending the pipeline to additional languages simply requires replacing these models with appropriate language-specific versions.
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Detection
End-to-End
Dataset Size
Backbone
Precision(1)
Recall(1)
F F1-Score(1)
None(t)
Full(1)
ResNet-50 (lle et al. 2016
77.67
65.91
71.31
1.81
1.96
20
Stahle Diffusion (Komaach er al.
2022
82.26
81.78
82.02
27.42
43.87
ResNet-50 (He
2016
83.24
74.52
78.64
17.03
28.70
100K
Stable Diffusion Konbach et al. 2022
84.55
82.09
83.30
32.29
45.77
Table 8: Comparsion of backbone features for training text-spotting module (Zhang et al.,
2022). We evaluate the text detection and recognition performance of each trained model SA- Text test set used in Tab. 2
B DIFFUSION FEATURES FOR TEXT-SPOTTING
As mentioned in Sec. we demonstrate that a text-spotting module can be trained directly using diffusion features for the text-spotting task (Zhang al., [022]] Ye al. 2023b Qiao al. 2024 Huang et al. 2024) instead of the commonly used ResNet (He et al. 2016) backbone, and that this approach not only enables effective training but also yields superior performance. To the best of our knowledge, this is the first work to demonstrate that diffusion features are suitable for effectively learning text-spotting.
B. EXPERIMENTAL DETAILS
We train TESTR Zhang et al. 2022) from scratch on subsets of SA-Text with a fixed timestep
t O, varying both the vision features and the dataset size evaluate their effects text-spotting performance. We use ResNet-50 (He et al. 2016). which serves as the vision backbone in the original TESTR (Zhang et al. 2022), along with diffusion features extracted from Stable Diffusion 2. 1 available on Hugging Face. We use model weights trained for 100K steps with a batch size of 8 and learning rate of le-4, using NVIDIA RTX 3090 GPUs. All other implementation details follow the default settings of TESTR (Zhang et al. 2022).
B.2 RESULT ANALYSIS
As shown in Tab. S with dataset size of 100K, diffusion- features demonstrate superior recognition performance compared to ResNet (He et al. (016) features. Importantly, when the dataset size is reduced to 20K samples, models relying on ResNet (He al. (016) features fail to effectively learn recognition, whereas diffusion features still achieve meaningful recognition learning even under limited data conditions. This shows that ResNet (He et al., 2016) features require larger amount of training data capture text semantics, which is consistent with the reliance on a large-scale synthetic dataset (Liu et al. 2020) in existing text-spotting methods (Zhang et al. 2022 Ye et al.) 2023b) Qiao al. 2024 Huang al. 2024).. In contrast, diffusion features, pretrained diverse image-text pairs, can offer improved adaptability to text recognition, even when trained with limited real-world data.
C LOss FUNCTIONS
Stage 1. Stage trains only the image restoration module using the following loss defined as:
Ezo, t, (w.((.) [Ile c0(2), 4, (,, c+)l
Lai
Stage 2. Stage 2 trains only the text-spotting module, while the restoration module remains frozen.
Following transformerbbseee text-spotting methods (Huang et al., 2024 Zhang et al. [2022 Qiao al. 2024), detection and recognition losses are computed via bipartite matching (Carion al. 2020). solved using the Hungarian algorithm (Kuhn 1955). Specifically, two separate loss functions
'stabi Stabilityai/se httalltttaiittalleaaaoa ffusi on-2-1 -base
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applied to the encoder and the dual decoder:
Lene Σ
(xtss [(m) + Abox ((m) + Agoo
L(m) ggott)
(2)
Liec Σ
Adols Lolis + Apoly Epoll + Nehar Ccbar
(3)
where m and n denote the numbers of instances with confidence scores exceeding a threshold T, and Less Loox, LoooU, Lpaay, and Lehar denote the text classification loss, bounding box regression loss, generalized IoU loss |Rezatofighi et al. 2019), polygon regression loss (LI), and character recognition loss (cross-entropy), respectively. Each loss is weighted by its corresponding factor: Acts- Abox. AgloU, Apoly. and Achar-
Stage 3.
the final training stage, we optimize both the diffusion- based image restoration module and the text-spotting module. The total loss function, with weight value X, is formulated as follows:
Caiff + A(Eene + Edce).
Additional details. Instance classification employs the focal loss (Lin al. 2017), which is the difference of positive and negative terms to handle class imbalance. For the j-th query, the classification loss is given by
c cls = 1 (jEIm(O)) a(1 - -B07 log())))
(ggl((()) (1 (1-au) (B) log(1 B6)),
where j(-) denotes the predicted confidence score, and Im(o) is the set of indices matched by the optimal bipartite assignment a between predictions and ground truth instances.
The control point regression loss encourages precise localization by minimizing the & distance between predicted and ground truth control points:
((U coord (jElm(())
Σ
(()
where d and d denote predicted and ground truth control points, respectively, and -1 maps prediction index j to its assigned ground truth index.
Character classification is formulated as a multi-clas problem and optimized via cross-entropy loss:
c(f)
Σ
(-r!
1o (()) log p(())
(j)Tm(a)
where pf is the predicted probability for class i, and r is the one-hot encoded ground truth label.
D IMPLEMENTATION DETAILS
Zhang et al. 2022; Huang et al., 2024) using standard detection and recognition metries. For detection, we report Precision (P). Recall (R), and F1-score (F), where detection is considered correct if its Intersection over Union (IoU) with a ground-truth box exceeds 0.5. For recognition, we adopt two lexicon-based evaluation settings: None and Full. The None setting evaluates recognition without any lexicon, requiring exact matches to ground-trune -trth transcriptions, reflecting performance scenarios. The Full setting permits matching predictions to the closest entry in ground-trui truth lexicon, simulating closed-vou voccauuauy conditions. This dual evaluation provides a comprehensive assessment of recognition accuracy.
Evaluation metrics. We evaluate text-spotting performance (Liu et al. 2020 2021
opec-voocablaryy
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ARCNet v2
dt 2021
TESTR
Zhang
2022
Deg. Level
Model
Detection
End-to-Eod
End-to Eid
Precision(()
Recall(f)
Fi TV-Scoon(()
Nonett>
Fullc
Precisions
Recalk()
D1 [1-Score
Nonett>
Fullo
DMTBER
al
3024
76.29
56.44
64.88
23.14
12.73
84.00
52.03
64.34
25.51
35.47
Levell
DUTBIR
53.01
51.86
52.45
15.26
20.71
16.75
22.82
ToReDiff
(067))
85.29
26.89
35.69
36.90
DuMBLR
Lingraa
2025
72.M5
53.94
62.05
19:60
27.52
79.69
50.50
6188
21.64
30.36
Level2
DidBBR
Lin
2024
53.25
51.18
52.21
14.75
19.61
58.85
50.18
54.17
16.15
21.45
TeReDuft
(Ours)
R3.02
$6.30
67.10
24.42
33.23
86,95
$2.86
65.75
26.39
35.13
DiMRER
ctl
2024
99.90
13.88
10.30
38.08
22.67
Level3
DUTBUR
La
10.15
ToRthbiff (OBrA)
81.76
44.11
57.30
19.61
27.50
42,02
56.13
19.92
28.34
Table 9: Text-spotting baseline comparison on SA-Text Each block presents text restoration performance under varying degradation levels, evaluated using two text-spotting models (Liu et al. 2021; Zhang et al., 2022). 'None' indicates recognition without the use of a lexicon, while 'Full" denotes recognition assisted by a full lexicon. The best results are shown in bold, and the second-best are underlined.
ABCNet v2 (Láu at 20211
TRSTR Zhning et nl. 2022
Model
Dotection
End to End
Detection
End to End
Precision/(1)
Recalk(
F1-Score(")
Nonett)
Fulkt)
Precision(())
Recall(?)
Fl-scure()>
None(t)
Full(")
DITBIR
66-14
59
62.71
33.75
40.05
76.33
68.35
30.27
46.11
DIABIR
Lin AT A.
2124
38.3
56,02
55.67
26
31.23
58.99
60.19
59.58
31.41
35.98
TeReDiff
TOurS)
83,95
67,58
74,88
48.39
55.01
$4.30
67.37
74,89
49.39
$6.45
Table 10: Text-spotting baseline comparison on Real-Text. We evaluate the text detection and
recognition accuracy of the restored images on Real-Text using text-spotring models (Liu et al.. [02TT] Zhang et al., 2022). None' indicates recognition without the use of lexicon, while "Full' denotes
recognition assisted by a full lexicon. The best results are shown in bold, and the second-best are underlined.
Model overview. Given a low-quality (LQ) image Ty E Hxxx3 the objective to recover a high- quality (HQ) image Ing E # x3 with enhanced visual and textual fidelity with H W 512.
The LQ image is first processed by lightweight degradation removal module (Liang et al.) 2021
and encoded by VAE (Kingma et al. 2013) to obtain conditional latent e E R #
4 x4 The
HQ image is encoded and perturbed with noise to produce noisy latent 2; R _ x4 These
are channel-wise concatenated to form the input condition e; R # W8 x8 Along with a diffusion
timestep f and text prompt embedding Pt E R where = 77 and d = 1024, is fed into the
diffusion-based image restoration module. In our setting, L = 9472 and D = 256 are used for the
multi-scale diffusion feature input F' E R Deformable attention ZZuu et al. 2020) is employed
to alleviate the high attention computation cost.
Following transformer- bassed text-spotting methods (hhang et al. 2022 Qiao et al. 2024 Huang et al. 2024) and inspired by DETR Caaionn et al, 2020),, two sets of queries, qdet E R and free E R4xD with Q = 100, are provided to the detection and recognition decoders Ddet and Drec respectively. These queries are processed through cross-attention with the encoder output in the respectful decoder layers. The resulting predictions are denoted as (d 1 and (r(6) JE=1,
{ where K is the number of
instances with confidence scores above threshold T = 0.5. Each d( (d()) ..... d(() represents (6)
a polygon with N = 16 control points, and y(() = (r1 ; .o......,.. .rf)) contains M = 25 recognized characters.
Training details. To construct high-quality (HQ) and low-quality (LQ) training pairs for SA-Text the LQ images were synthesized using the default degradation settings from the Real-ESRGAN pipeline (Wang et af., 2018). The final model used for performance evaluation the main paper was trained on four NVIDIA H100 GPUs, with batch size of 32 per GPU. Each of the three training stages (Stage I to Stage 3) was trained for 100,000 iterations. The hyperparameters for the text-spotting encoder loss function 2 decoder loss function 3, and stage3 loss function 4 are set as follows: Asta = 2.0, Apuord = 5.0, Avhar = 4.0, Agiou = 2.0 and À = 0.01. During training, only the attention layers are updated in the image restoration module, whereas the text-spotting module undergoes full fine-tuning. The number of inference sampling steps for the diffusion based image restoration module was set to 50.
22

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Published as a conference paper at ICLR 2026
TESTR
TISTR
Model
That Candittion
Detectio
End Eed
Model
Tixt Conditiion
Detectio
End-a Bed
Preciooal")
Recaliit)
Fi-Seartt)
F1-Screth)
Nonett)
Falk
Nud
82.09
90.27
62.16
25.36
31.86
Nul
81.22
42.52
59.99
21.24
20. 29
Sugel
Captooer (hom))
$3344
$2.62
68.30
22.31
15.8%
Sugge
Captoner tlame)
69.53
63.39
26.76
11.71
7538
Gourdovw
26.12
Soggc
26.00 .0.00
63.28
33,09
34.1
44387
Groznd-zzut
86.18
61,00
7188
33.31
43.00
(a) Se-Text (Level 1). Results evaluated the SA-
(b) Se-Text (Level 2). Results evaluated on the SA-
Text dataset with Level degradation.
Text dataset with Level degradation.
TESTR Zhungg et 2002
TESTR
Zhang al (022
Model
Coodition
Text
End
Presieunl")
Rocilit))
F1:Scomt))
None"))
Falk
Presixunt))
Raaaltty
F1-Sccm
NunclT)
Falk
75
(llmn)
41.61
$1.23
44.73
$1.28
Ground-uut
$0.00
55.94
06.84
27,00
37,57
Ground suth
83.46
73.61
78.35
$2.59
59.28
Yul
77.04
47.91
58.88
17.44
24.5
Null
$1.05
70.22
25.28
46.09
52.78
9oge3 Charno-math
Capioter (Dur)
$4.30
12:00
16.11
10
16.36
Sege1 Captonerr (Dare)
$4.30
76:00
09.38
16.15
00.04
30.44
65.51
Chimrd-ma
$1.41
5438
(c) SA-Text (Level 3). Results evaluated on the SA-
(d) Real-Text. Results evaluated on the Real-Text
Text dataset with Level 3 degradation.
dataset.
Table 11: Additional ablations for SA-Text and Real-Text. Extending training to Stage 3 enables the model learn text-aware features, thereby improving text restoration performance. Moreover, conditioning with prompts from LLaVA (Stage 1) or our text-spotting module (Stage 3) yields further improvements compared null prompts.
Datasct
Model
PSNR(())
SSIM(T)
LPIPS())
DISTS())
FIDX))
NIQE(J)
MANIQA(T)
MUSIQIT)
CLIPIQA(T)
Real-ESRGAN
20.30
0.5967
0.3177
0.2116
56.68
4.626
0.5979
68.81
0.5245
SwinIR
19.90
0,5852
0.3286
0.2159
57.41
4,790
0.5661
67.02
0.5015
ResShift
0.5825
0.4699
StableSR
19.58
0.5105
0.3614
0.2217
49.34
4,418
0.6036
66.88
0.5077
SA-Testae
DiMBIRR
DilTBI
19.58
0.1965
0.4638
0.4001
0.3636
0.2086
45.10
5,107
0.6771
73.33
0,6589
0.6345
SecSR
19.72
0.5671
0.3173
0.1943
40.14
5,050
0.6730
73.57
0.6190
SUPIR
16.81
0.4084
0.4723
0,2554
56.05
5.626
0.6723
70.10
0.5980
Fanhh!Mf
19.78
0.5444
0.7075
0.6630
TeReDiff (Ours)
19.71
0.5717
0.2828
0.1702
36.94
5,152
0.6471
72.07
0.6145
Real-ESRGAN
23.84
0,8062
0
0,2040
68.
6.156
0.5437
58.72
0.2507
SwinIR
24.31
0.8122
0,2314
0.2049
67.19
6.799
0,4905
55.39
0.3609
ResShaft
0.7989
0.2936
0.2559
0,4940
StableSR
24.20
0.7745
0.2744
0.2225
58.81
7.107
0.5394
$7.85
0,3821
Real-Text
DiMBIR
DifTBIR
22.70
0.6365
0.4090
0.2849
81.56
75.78
6.842
0.6194
0.5937
67.28
0,5748
SeeSR
23.63
0.7858
0.2852
0.2477
79.75
7.259
0.6001
67.52
0.5452
SUPIR
16.64
0.3483
016405
0.3579
99. 13
14.20
0.6255
63.13
0.4842
FaihDDif
67.15
0,5350
TeReDiff (Ours)
23.37
0.7849
0.2848
0.2386
68.94
7.643
0.5637
62.02
0.4545
Table 12: Quantitative results of image restoration. We evaluate the image quality of our TefeDiff
compared with other baseline models. The degradation pipeline (Wang et al. 202I) used in prior works is applied. DifIBIR (Lin et al.) 2 (denoted as v2.1 in Fig. [ uses GitHub-released weights that are further trained from those in the original paper.
E ADDITIONAL QUANTITATIVE RESULTS
E. EXTENDED IMAGE QUALITY COMPARISON
Tab. 12 shows the additional results for all baseline models on image restoration metrics. It is clear that our proposed task, TAIR, does not negatively affect overall image restoration performance, while improving upon the model's 's ability to accurately restore text. We posit that these image restoration metrics are not an ideal measure for evaluating text fidelity or legibility, which is the main focus of this paper.
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Published as a conference paper at ICLR 2026
E.2 EXTENDED BASELINE COMPARISON
Our main baseline image restoration model is DIABBI (Lin et al. 2024). We further include comparisons with Dif'BIR (denoted as DiffBIR v2.1), which leverages publicly available weights from the official GitHub repository that have undergone additional ane-tuning beyond what was reported in the original publication. The comparative results on text restoration performance are provided in Tab. for SA-Text and Tab. 10 for Real-Text.
E.3 EXTENDED ABLATION EXPERIMENTS
Extending the analysis in Tab. 6 of the main paper, present additional ablation results in Tab. II including evaluations across the three degradation levels on SA-Text and on the Real-Text dataset. The table compares (1) two model training stages, referred to Stagel and Stage3, and (2) three prompting strategies for the image restoration module: Null (no prompt), Captioner (LLaVA VA or our text-spotting module), and Ground-truth (ground truth text). The ground truth prompt is constructed from the actual textual content present in the LQ image, whereas prompts of "Captioner" use recognized texts extracted from the LQ image by our text-spotting module or a conventionally used LLaVA (Liu et al. 2023b).
Overall comparison. Comparing Stage 1 and Stage under Null prompt text conditioning, we observe accuracy gains of +4.27%, +2.93%, +2.43%, and +2.08% in Tab. la through Tab. Tid respectively, indicating the benefit training with text-aware supervision in Stage 3. When using captioner, we employ the LLLVV (Liu al. 20235) captioner for Stage due the absence of a trained text-spotring module, whereas Stage 3 leverages our trained text-spotting module as the captioner. The improved performance of Stage 3 demonstrates that our module provides more accurate and text-aware prompts, enhancing restoration quality. Finally, when utilizing ground-truth texts from LQ images for both stages, substantial performance improvements are observed with minimal differences between them. The ideal scenario in which the restoration module is provided with exact textual information not achievable practice and primarily serves establish an upper bound on performance.
Comparison on SA-Text degradation levels. Comparing Stage pr and Stage3 pr across the three degradation levels of SA-Text, observe that the performance gap increases with higher degradation severity. This highlights the importance of Stage3, which learns text-aware features and enables prompting the restoration module using our text-spotting module, proving these prompts' superiority over those from an external LLaVA captioner.
E.4 USER STUDY EVALUATION
Criteria
DiffBIR
Ours
Text Quality
2.20%
97.8%
Image Quality
9.2%
90.8%
Table 13: User study text and image restoration quality.
To evaluate the quality of both text and image restoration achieved by our TeReDiff, we conducted a simple user study comparing it with our baseline, DifIBIR (Lin et al., 2024p. The study included 10 samples: 5 from SA-Text Level 3 and 5 from Real-Texe A total of 50 participants took part in the evaluation. Samples for SA-Text and Real- Text were selected from the examples shown in Tab. Tab. 3 respectively. As shown in Tab. 13 the user study results indicate that our TeReDiff outperforms the baseline both text restoration and visual quality. These results highlight that humans often consider text semantics when evaluating image quality, an aspect not fully captured by existing image metrics.
Each participant evaluated the samples based on the following set of questions.:
1. Which image better restores the text content? (Image 1 Image 2)
2. Which image better restores the overall appearance? (Image 1 Image 2)
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Published as a conference paper at ICLR 2026
LQ
HQ(GT)
LQ
HQ(GT)
Embretal
Embratel
NOB
NOB
COO
COG
Image
Image 2
Image 1
Image
WORD
FONDES
Emoratel
Emoratel
NOB
NOB
Figure 10: Example samples for user study.
F ADDITIONAL QUALITATIVE RESULTS
Fig. 1 Fig. and Fig. 13 we show further qualitative results on text-aware image restoration (TAIR). The results on Se-Text across different degradation levels and those on Real-Text, demonstrate that our model outperforms other diffusion- -based methods.
In Fig. Fig.
Fig. 16 shows the restoration results of TeReDiff on three difficulty levels (easy, medium, hard) from TextZoom (Wang et al. 2020). TeReDiff demonstrates strong text restoration performance, producing results that are even clearer than the ground-truth high-resolution images.
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Published as a conference paper at ICLR 2026
LQ image
StableSR
SeeSR
SUPIR
Faith
TEUTING
TSUTAYA
DLBEST FOORED
TSUTATA
DiffBIR
DiffBIRv2
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GT
TSUINTA
TSUTAYA
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LQ Image
StableSR
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SUPIR
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ascs
SSTES
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SUPIR
FaithDiff
COMM TO THE REPUALL
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GT
COME IEPLALIC
WELCOME THE REPUBLLE
WELCOMI THE REPURLIE
.COMI THE REOPH UIILIC
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FaithDiff
LJUIS NUIITTON
JOUIS YUITTON
LOUIS VUITTON
LOUIS YUITTON
COURS YURTTO
DiffBIR
DiffBIRv2.1
Ours
GT
LOUIS VINTTON
LOUIS VUITTON
LOUIS VUITTON
LOUIS VUITTON
LQ Image
StableSR
SeeSR
SUPIR
Fifth
APOTH
APOTHECARS
M MiPP ARY
APOTHECAIN
APOTE MOTEEEGNRY
DifiBIR
DiffBIRv2.1
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POTHECARR
APOTE
APOTHECARY
APOTHECARY
Figure 11: Qualitative results on SA-Text test set Level 1.
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Published as a conference paper at ICLR 2026
LQ Image
StableSR
SeeSR
SUPIR
Faith
AOSSITA 3
O 9 IYA
RODWIYA
ROSHIYA
ROSSIVA
DiffBIR
DiffBIR22.1
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GT
NOSSIYA ROSSSYA ROSSIYA
ROSSIYA
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StableSR
SeeSR
SUPIR
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IDOCIHL
moqIO
MaKET
DiffBIR
DiffBIRv2.1
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LQ Image
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FaithDiff
BELVEDEBE
BRAVEREDE
MA
I DTT
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DiffBIR
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GT
DONEDELE
HELYEDERR
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LQ Image
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FaithDiff
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iphone
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DiffBIR
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CAETILS CACtut 1&c
EOTTAAE BARK
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Cactus Jaca Cacals JHob Jacm
Cactus Jack
Figure 12: Qualitative results on SA-Text test set Level 2.
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Published as a conference paper at ICLR 2026
LQ Image
StableSR
SeeSR
SUPIR
FaithDiff
mowsiw
FisOWSEat
Mevrani
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DiffBIR
DiffBIRv2.1
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LQ Image
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EMANONI
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LQ Image
StableSR
SeeSR
SUPIR
FaithDiff
CASINO
CASIND
CASINO
CASINO
DiffBIR
DiffBIRv2.1
Ours
GT
CASINO
CASINO
CASINO
CASINO
Figure 13: Qualitative results SA-Text test set Level 3.
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Published as a conference paper at ICLR 2026
LQ Image
StableSR
SeeSR
SUPIR
Faith
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Figure 14: Qualitative results Real-Text (1/2).
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Figure 15: Qualitative results on Real-Text (2/2).
30

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DESECHO Published as a conference paper at ICLR 2026
Easy Setting
Camille Mango
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Figure 16: Qualitative results on three difficulty settings of TextZoom.
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Published as a conference paper at ICLR 2026
G DISCUSSION
G.1 VLM CORRECTION
ABCNet v2
(Lu 2021
TESTR
Zhung all 2022
Model
Detcction
Fad-a-FFnd
Donection
Fad-tn-Fndd
Precisiont() 11
Recalle")
F1 So
Nonecti
Fatic))
Precasooi"))
Recallify
P- Sconee")
Nonett)
Full()
TeReDDif (wio VLMI
N3.95
67.58
74.85
48.39
$5.01
$4.30
67.37
74.89
49,39
56.45
TeReDiff (we VIM)
83.70
68.28
75.21
49.70
55.99
85.87
67.74
75.74
50.87
57.25
Table 14: VLM correction results on Real-Text. Employing VLM (Bai et al. 2025) to refine the text predictions generated by the text-spotting module further improves text restoration performance.
Providing the diffusion--bssd image restoration module with accurate textual prompts is essential for achieving faithful and precise text restoration. Although our TeReDiff framework incorporates text-spotting module to generate such prompts and has demonstrated effectiveness, erroneous text predictions may cause the restoration module to reconstruct incorrect textual content. To address this limitation, we further investigate the use of a vision- languuge model (VLM) to refine the predictions of the text-spotting module. Since the spotting module lacks the ability to fully capture character- level dependencies and contextual relationships within the predicted text, we leverage the linguistic knowledge of VLMs to correct these predictions. The quantitative restoration results obtained using the 3B Qwen2.5-VI VL (Bai al. 2025) correct text predictions on Real-Text reported in Tab. [4
For the VLM, two inputs are provided:
1. Visual input: The restored image each denoising timestep.
2. Textual input:
The image contains degraded or low-quality text. The OCR-predicted text may contain errors. Use both the visual appearance of the text and the predicted text to infer and correct the actual text. Only recognize and correct English text.
OCR prediction: "[Text prediction from the TS module]" Return only the corrected English text from the image.
G.2 COMPUTATIONAL COMPLEXITY AND INFERENCE TIME
Criteria
TeReDiff (Ours)
DiMBIR (Baseline)
SeeSR
StableSR
FaithDiff
Model Size (M)
1706.20
1666.75
2283.70
1409.11 11.18
2612.20
Inference Time (s)
7.29
6.59
7.24
9.97
Table 15:
Comparison of model sizes and inference times across different methods. Experiments were conducted on an NVIDIA RTX 3090 GPU.
TeReDiff has a total of 1, 706.2M parameters, comprising 1, 682.54M in the image restoration module and the remaining 23.65M in the text-spotting module, which is increase of only ~ 1.4% over the backbone alone. Despite the addition of a text-spotting module to an existing restoration module, this leads to negligible increase computational overhead. As result, TeReDiff attains model size and inference time par with (and sometimes better than) restoration-on similar larger capacity. As shown in Tab, [5] TeReDiff is smaller and/or faster than all compared methods except the backbone baseline which it is built, while delivering substantially better text restoration. This demonstrates that TeReDiff improves text-aware image restoration at minimal computational cost, underscoring its efficiency and real-world applicability.
baselines of
G.3 ANALYSIS OF FAILURE CASES
To further investigate the limitations our method, we analyzed the "None" results from Tab 2 TESTR (Zhang al. 2022) evaluation and report the minimum text sizes in Tab. T6, Specifically,
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Tab. 16 shows the minimum bounding box sizes of text that either correctly recognized by the text spotting module at the initial denoising step (Case 1) or accurately restored by TeReDiff (Case 2).
SA-Text (Lv1)
SA-Text (Lv2)
SA-Text (Lv3)
Real-Text
Case
16x17
16x17
28x22
20x16
Case
14x27
25x20
35x26
20x17
Table 16: Minimum text box sizes (HxW) for accurate text restoration. Evaluated on 512x512 images from SA-Text and Real-Text. Case denotes text detection outputs of the Text Spotting Module, while Case 2 denotes the text restoration results based on the E2E (None) Fi-score of TESTR (Zhang et al. 2022).
We statistically observed tendency for smaller text to be either more difficult to spot (Case I) or harder to successfully restore (Case 2). However, various types of noise are randomly applied by the degradation kernel from Real-ESRGAN (Wang al. [021] ) and other additional factors, such the specific font and text length, may also affect the restoration results. The experiments Tab, [6 help identify the practical boundaries of text size at which the text spotting module fails to detect text and the restoration module fails to recover it under various degradation scenarios.
H USAGE OF LARGE LANGUAGE MODELS
Following the ICLR 2026 submission policy, we disclose that the use of Large Language Models aids in correcting grammar and editing LaTeX syntax.
33


Reviews:
[{"review_id": "e4yjkt7lPZ", "reviewer": "Reviewer_YnwB", "summary": "This paper proposes TeReDiff, a text-aware image restoration framework that effectively recovers readable text in severely degraded full-scene images while preserving overall visual quality. Unlike prior scene text image super-resolution (STISR) methods that focus on small, single-word crops, TeReDiff addresses the more challenging and general Text-Aware Image Restoration (TAIR) task. By incorporating multi-stage training and text-conditioned diffusion, the model mitigates text hallucination and achieves good performance on both text detection/recognition metrics and general image quality benchmarks.", "questions": "Please refer to Weaknesses.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- Introduces TAIR—a more realistic and general setting than existing STISR—by restoring full-scene images with multiple, diverse text instances.\n- Significantly reduces text hallucination and improves readability through text-conditioned diffusion and multi-stage training.\n- Achieves good performance on both text recognition metrics and standard image restoration benchmarks, demonstrating balanced fidelity and perceptual quality.", "weaknesses": "- The evaluation relies heavily on synthetic or curated datasets (e.g., SA-Text), which may not fully reflect the complexity and variability of real-world degraded images.\n- Lacks in-depth discussion of scenarios where the method fails (e.g., extremely low-resolution or occluded text), reducing insight into limitations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Re2KMCmpWz", "reviewer": "Reviewer_Amhv", "summary": "This paper presents Text-Aware Image Restoration (TAIR), designed to mitigate diffusion models’ tendency to hallucinate text in degraded images. The authors introduce SA-Text, a dataset of 100K images with detailed text annotations, and TeReDiff, a diffusion-based framework that integrates text spotting for joint training and text-conditioned denoising. Experimental results demonstrate improved text fidelity compared with existing approaches on the SA-Text and TextZoom benchmarks. However, the method’s dependence on synthetic degradations, limited validation on real-world data, and insufficient analysis of computational overhead reduce its practical significance.", "questions": "1. When the text-spotting module fails at early denoising steps (e.g., misreads \"HELLO\" as \"H3LLO\"), how does TeReDiff recover?\n\n2. Ablations suggest SA-Text drives performance. If trained on TextOCR, would TeReDiff still outperform DiffBIR?\n\n3. Why not test on non-English text? Can the VLM filtering in SA-Text handle languages with complex scripts?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The author clearly identifies a critical limitation in diffusion-based image restoration—its inability to accurately recover text regions. To address this, the author introduces Text-Aware Image Restoration (TAIR) as a novel task that jointly optimizes visual quality and text fidelity, offering strong potential for practical applications.\n\n2. The author constructs a dataset of 100K high-resolution images derived from SA-1B, densely annotated with text polygons and transcriptions through a scalable VLM-based annotation pipeline.", "weaknesses": "1. The author primarily conducts training and evaluation on synthetic degradations (e.g., Real-ESRGAN). Moreover, while the Real-Text results (Table 3) demonstrate modest improvements (e.g., a +6% F1-score over FaithDiff), the absence of corresponding visual examples limits the clarity of qualitative gains.\n\n2. The author provides no analysis of error propagation across the pipeline. Given that the method relies on accurate text spotting, recognition errors are likely to propagate and compromise restoration performance, but this crucial aspect remains unexplored.\n\n3. Jointly training the text-spotting module with the diffusion backbone is conceptually straightforward. However, the ablation studies (Table 6) indicate that most of the observed improvements stem from the inclusion of the SA-Text dataset and the use of multi-stage training, rather than from the joint optimization itself.\n\n4. The evaluation depends exclusively on text-spotting metrics (e.g., F1-score), which fail to reflect the semantic accuracy of restored text. Such metrics emphasize detection precision and recall but overlook whether the recovered text is contextually or linguistically correct, thereby limiting the reliability of the reported improvements.\n\n5. Although SA-Text is described as a “language-agnostic” annotation pipeline (Section 3), the experiments and validations are conducted exclusively on English text. The absence of cross-lingual evaluations leaves the claimed language generality unverified.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EtK1UvjXPq", "reviewer": "Reviewer_5iBr", "summary": "This paper addresses the critical challenge of text-image hallucination in diffusion models for image restoration, proposing a novel task called Text-Aware Image Restoration (TAIR). To advance this field, the authors constructed a large-scale benchmark with dense text annotations named SA-Text dataset. The key innovation is the TeReDiff model, where the joint training of a text-spotting module with the restoration module enables text-aware supervision during restoration, achieved new state-of-the-art results on TextZoom and demonstrated superior super-resolution results.", "questions": "1. Can the authors provide qualitative, direct metrics or a feasible evaluation scheme? The core objective of the newly proposed task is text super-resolution guided by solving the text-image hallucination problem. However, the paper also states that no existing metrics can accurately measure the effectiveness in addressing this issue. The proposal of a task with no clear way to determine if it has been completed lacks meaningfulness.\n2. Firstly, the comparison involves too few methods, which undermines the credibility of the claimed superiority in image quality. Secondly, it is unclear why most performance metrics for the \"further trained DiffBIR\" model are significantly lower than those of the original DiffBIR. Moreover, the fact that this model was not included in the comparisons of Tables 2/3。 This counterintuitive result requires clarification.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. Excellent originality: This paper purposefully introduces a new task (TAIR), along with a corresponding dataset and a SOTA model capable of performing both tasks simultaneously.\n2. Good quality and performance: The paper constructs a large-scale, high-quality dataset for TAIR and designs a novel model architecture that leverages joint training for both text-spotting and restoration tasks, achieving outstanding performance across multiple benchmarks.\n3. The paper is well-written and easy to follow.", "weaknesses": "1. While the paper emphasizes that the new TAIR task differs from previous models by focusing on text-image hallucination (text readability), it fails to sufficiently demonstrate the model's superiority in this core aspect. The evidence is limited to a few comparative images and existing metrics that are not fully relevant. This lack of qualitative comparison methods specific to the TAIR task undermines the credibility of its performance evaluation.\n2. The comparison in Table 4 is insufficient to substantiate the claim of superior image quality, as it includes too few diffusion-based SR models. To properly validate performance, the evaluation should be expanded to include more competing methods, particularly established models like StableSR and ResShift that are already discussed in Table 2.\n3. The provided figures in Table 2 and Table 3 contain multiple inaccuracies in labeling \"Best results\" and \"second-best\"， which is confusing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dI4bBRAJsv", "reviewer": "Reviewer_cLfC", "summary": "This paper introduced the Text-Aware Image Restoration (TAIR) task, which requires simultaneous recovery of visual content and textual fidelity. And proposed corresponding diffusion-based method TeReDiff, which leverages text-spotting task to supervise intermidiate features and guide the optimization process. Also the paper built high-quality scene images SA-Text benchmark with dense annotations of diverse and complex text instances. Extensive experiments demonstrate the effectiveness of the proposed method.", "questions": "1. Does the proposed method only support English text? How does it perform on non-Latin characters?\n\n2. Would the optimization objectives of the TAIR task affect the restoration of images without text, for example, causing tree branches to be mistakenly reconstructed as characters?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Strengthens:\n1. Proposed an exhaustive pipeline for building the dataset and made the large-scale high-quality scene images SA-Text benchmark, contributing to the advancement of research community.\n2. Introduced text-spotting module to supervision on intermediate features, explicitly guides optimization and empowers the model to implicitly perceive textual information from LQ inputs.\n3. The proposed method achieves outstanding performance on TAIR, surpassing the existing Real-SR methods.", "weaknesses": "Weaknesses:\n1. The novelty is limited. \n    - The proposed method integrates a plain ControlNet to inject LQ priors, without any specific design tailored for the text-focused nature of the task. \n    - The detail structure of Text-spotting module like optimization objective also has not been fully described, which undermines the reproducibility. \n    - This paper is not the first to define the TAIR task, as earlier work [1] have already proposed similar tasks.\n    References:\n    [1]Q. Hu, L. Fan, Y. Luo, Y. Yu, X. Guo, and Q. Fan, “Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders,” CoRR, vol. abs/2506.04641, 2025, doi: 10.48550/ARXIV.2506.04641.\n2. The experiments are insufficient. \n    - TAIR focuses on both visual content restoration and textual fidelity. However, the paper lacks comprehensive evaluations on real-world image super-resolution tasks. Moreover, the metrics individually assess either scene reconstruction quality or text restoration quality, and thus fail to reflect the balance between these two aspects. \n    - While the visual results in the paper are impressive, the examples contain only a single line of text with limited style variation, which makes it difficult to demonstrate the effectiveness of the proposed method in real-world scenarios with diverse text densities and styles.\n3. The layout is unsatisfactory. The gap between the text and corresponding tables/figures is too large, making readers hard to follow the content, e.g. Figure 3, Table 2.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)