PlantRSR: A New Plant Dataset and Method for Reference-based Super-Resolution
Authors: Hongyang Zhou
Xiaobin Zhu
Shengxiang Yu
Liuling Chen
Jingyan Qin
Xu-Cheng Yin
Venue: ICLR 2026 Poster (2026)
Keywords: Plant dataset
Image Super-Resolution
Reference-based Super-Resolution
Primary area: datasets and benchmarks

Abstract: Single image super-resolution (SISR) often struggles to reconstruct high-resolution (HR) details from heavily degraded low-resolution (LR) inputs. Instead, reference-based super-resolution (RefSR) methods offer an alternative solution to generate promising results using high-quality reference (Ref) images to guide reconstruction. However, existing RefSR datasets focus on limited scene types, primarily featuring
human activities and architectural scenes. Plant scenes exhibit complex textures and fine details, essential for advancing RefSR in natural and highly detailed scenes. To this end, we meticulously captured and manually selected high-quality images containing rich textures to construct a large-scale plant dataset, PlantRSR, comprising 16,585 HR–Ref pairs. The dataset captures the complexity and variability of plant scenes through extensive variations. In addition, we propose a novel RefSR method specifically designed to tackle the distinct challenges posed by plant imagery. It incorporates a Selective Key-Region Matching (SKRM) that selectively identifies and performs matching between LR and Ref images, focusing on distinctive botanical textures to improve matching efficiency. Additionally, a Texture-Guided Diffusion Module (TGDM) is proposed to refine LR textures by leveraging a diffusion process conditioned on the matched Ref textures. TGDM is effective in modeling irregular and fine textures, thereby facilitating more accurate SR results. The proposed method achieves significant improvements over state-of-the-art (SOTA) approaches on our PlantRSR dataset and other Benchmarks.

Published as a conference paper at ICLR 2026
PLANTRSR: A NEW PLANT DATASET AND METHOD FOR REFERENCE-BASEL SUPER-RESOLUTION
Hongyang Zhou', Xiaobin Zhu' Z Yu²,, Liuling Chen',
Jingyan Qin', Xu-Cheng Yin' I 'University Of Science and Technology Beijing, Innstitute of Botany, Chinese Academy of Sciences
ABSTRACT
Single image super-resolution (SISR) often struggles to reconstruct high- resolution (HR) details from heavily degraded low-resolution (LR) inputs. In- stead, reference-based super-resolution (RefSR) methods offer an alternative so- lution to generate promising results using high-quality reference (Ref) images to guide reconstruction. However, existing RefSR datasets focus on limited scene types, primarily featuring human activities and architectural scenes. Plant scenes exhibit complex textures and fine details, essential for advancing RefSR in natu- ral and highly detailed scenes. To this end, we meticulously captured and man- ually selected high-quality images containing rich textures to construct large- scale plant dataset, PlantRSR, comprising 16,585 HR-Ref pairs. The dataset captures the complexity and variability of plant scenes through extensive varia- tions. In addition, we propose novel RefSR method specifically designed tackle the distinct challenges posed by plant imagery. It incorporates a Selective Key-Region Matching (SKRM) that selectively identifies and performs match- ing between LR and Ref images, focusing on distinetive botanical textures to improve matching efficiency. Additionally, Texture-Guided Diffusion Mod- ule (TGDM) is proposed to refine LR textures by leveraging a diffusion process conditioned on the matched Ref textures. TGDM is effective in modeling irreg- ular and fine textures, thereby facilitating more accurate SR results. The pro- posed method achieves significant improvements over state-of-the-art (SOTA) ap- proaches on our PlantRSR dataset and other benchmarks, Code and dataset are released athht /ttp//ggttttttttte///
/tthttts://gitttbb.cmm
INTRODUCTION
SISR aims reconstruct an HR image from an LR input, and has been widely applied in various fields, including critical uses in plant phenotyping, where high-resolution imagery crucial for detailed morphological analysis, disease diagnosis, and growth monitoring in precision agriculture.
While SISR has shown promising results Dai et al. (2019) Niu et al. (2020); Huang et al. (202 2026); Wang et al. (2024a); Guo et al. (2025), it often fails to reconstruct precisely detailed textures of the ground-truth HR image when the original high-frequency information is severely lost during the degradation. To address this issue, RefSR methods Zhang et al. (2019); Yang el al. (2020); Lu et al. (2021): I): Jiang et al. (202I); Xia al. (2022); Cao et (2022); Zhou et al. (2025) incorporates external Ref images guidance, leveraging external high-quality textural details to enhance texture recovery.
Kong et al. (202I): by; Wang et al. (2021); Zhou et al. 2023
Recent RefSR methods leverage feature aligmment, attention mechanisms, or implicit correspon- dence learning to fuse textures from Ref images. Despite significant progress, existing studies pri- marily focus on limited scenes such as human daily life and architectural structures, as reflected by datasets like CUFED5 Zhang et al. (2019) and LMR Zhang et al. (2023). As shown in Fig. (a) and (b), CUFEDS and LMR mainly contain scenes with rigid structures and limited geometric variations.
In contrast, plant scenes, as shown in Fig. D (c), exhibit distinct characteristics such significant shape deformation, subtle texture variations, and frequent defocused backgrounds, which introduce
Xiaohin Zhu and Shengxiang Yu the corresponding authors.

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additional challenges for RefSR. Moreover, the lack of publicly available RefSR datasets tailored to plant imagery restricts the development of methods capable of handling its unique challenges. Although the DRefSR Zhou et al. (025) expands upon CUFEDS by increasing data diversity and includes certain plant images, it is not specifically designed for plant scenes and suffers from in- sufficient quantity and variety. As result, existing approaches often struggle to generalize such complex and diverse scenarios. In particular, plant imagery presents unique challenges, such as ir- regular structures and diverse textures, that differ significantly from the relatively structured content found in current datasets. This highlights the necessity for RefSR datasets and strategies specifically tailored to plant imagery.
HR
Ref
(a) CUFEDS dataset
(b) LMR dataset
(c) Our PlantRSR dataset
Figure 1: Example image pairs from different RefSR datasets. (a) CUFEDS dataset Zhang et al.
2019), primarily featuring scenes of human daily activities. (b) LMR dataset Zhang et al. (2023), focused on architectural scenes. (c) Our PlantRSR dataset, designed for complex botanical textures with diverse plant species.
To address the above challenges, we construct a novel large-scale RefSR dataset specifically de- signed for plant imagery, named PlantRSR. All images were carefully captured by us from real- world plant scenes, ensuring high authenticity and diversity. The dataset comprises 6,134 high- quality HR-Ref image pairs with extensive variations in color, rotation, deformation, and back- ground blur, covering resolutions from 2K to 8K. In RefSR tasks, models are commonly trained using cropped image patches, since full-resolution images often too large for direct processing. However, generating aligned patch pairs is particularly challenging. Automated patch generation in previous datasets often leads to misaligned or semantically inconsistent pairs, which is unsuitable for complex plant imagery. To ensure high-quality training data, we manually annotate and con- struct 16,585 semantically aligned Ref patch pairs, exceeding the 11,817 pairs in the widely used CUFED5 dataset. Besides, the PlantRSR dataset includes a testing set of 100 image pairs, offering the first RefSR benchmark for plant imagery and a solid foundation for future research.
We introduce an innovative RefSR approach tailored to address the distinct challenges in plant en- vironments. First, we propose Selective Key-Region Matching (SKRM) that can target the focal characteristics of plant images, performing matching only on key regions between LR and Ref im- ages to significantly enhance matching efficiency. Second, we present a Texture-Guided Diffusion Module (TGDM), which enhances LR features by incorporating matched Ref textures as conditional guidance within a diffusion-basee refinement framework. This novel module effectively leverages the fine- enainndd and irregular textures from the Ref image to improve the quality of the SR image. Together, these components form a unified framework that ensures both accurate texture transfer and efficient feature enhancement. Extensive experiments demonstrate the superiority of our method. In summary, our main contributions are four-fold:
We contribute new large-scale plant dataset for RefSR, named PlantRSR, which contains 16,585 high-quality training pairs with rich variations in color, rotation, deformation, and background blur, reflecting the complexity of real-world plant scenes.
We propose a novel RefSR method specifically designed to tackle the challenges posed by plant imagery. Extensive experiments multiple datasets demonstrate that method outperforms SOTA methods both quantitatively and qualitatively.
We propose Selective Key-Region Matching (SKRM) module that leverages the focal characteristics of plant imagery to perform region-specffic matching between LR and Ref images, thereby significantly enhancing matching effectiveness.
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We propose Texture- Guidee Diffusion Module (TGDM) that introduces diffusion mech- anism conditioned on matched Ref textures to refine LR regions, enabling more accurate reconstruction of the SR image.
2 RELATED WORK
Reference-based Image Super-Resolution. Unlike conventional SISR methods that rely exclu- sively on the LR input, RefSR incorporates an additional high-quality Ref image to supply textures and structural details. Pioneer RefSR methods mainly emphasized alignment between the LR and Ref images. For instance, Wang et al. Wang et al. (2016b) proposed iterative non-uniform warping to refine the Ref for better texture extraction. CrossNet Zheng et al. (2018) employs multi-scale optical flow to achieve spatial alignment, while SSEN Shim al. (2020) adopts deformable convo- lutions (Dai et al. 2017 Zhu et al. 2019) to align feature representations adaptively.
In addition to alignment-based strategies, significant body of work has explored patch-based tex- ture transfer. SRNTT Zhang et al. (2019), for example, utilizes perceptual features extracted from VGG|Simony & Zisserman (2015) to identify semantically similar textures between LR and Ref images. E2ENT2 Xie et al. 2020) proposes a task-specific feature extraction framework, moving beyond general classification features. TTSR TTng al. (2020) designs cross-scale transformer to effectively aggregate textures across multiple levels. Other approaches, such as AMRSR Pesavento al. (2021) and CIMR-SR Yan et al. (2020), expand to multi-Ref settings for richer texture guid- ance. MASA Lu et al. (202 l) addresses efficiency through a hierarchical coarse-to-fine matching scheme. To tackle domain discrepancies in resolution and content, -2MMathhnng Jiang et al. (2021)
introduces contrastive learning He et al. (2020) and knowledge distillation Hinton et al. (2013) for
robust correspondence learning. At the same time, DATSR Cao et al. (2022) leverages transformer architectures for better handling of scale and transformation variations. RRSR Zhang et al. (2022)
proposes a reciprocal mechanism where reconstruction feedback enhances the overall performance. Ref-IRT Zhang et al. (2024) introduces a progressive restoration pipeline for handling complex degradations. MCMSR Zheng et al. (2024) focuses on discovering multiple matching candidates in the Ref image for each LR region to improve completeness. SSMTF Zhou et al. (2025) attempts to leverage state-space models to extract mullii-cale information from Ref images. Recently, diffusion models have emerged as powerful paradigm for leveraging Ref information. DiftMSR Li et al.) 2024a) employs diffusion to generate prior knowledge from Ref for magnetic resonance image re- construction. Similarly, in remote sensing. Ref-Diff Dong al. (2024) proposes a change-aware diffusion model to extract change priors from Ref images. CoSeR Sun et al. (2024), instead of using a physical Ref, utilizes a diffusion model to generate a semantically relevant Ref image aid recon- struction. This idea of generating references connects to retrieval- augmented methods; for example, RAG Lee et al. (2025) proposes Retrieval-Augeee -ergeeell- Generation framework that retrieves a Ref image given LR query. However, most existing methods still face limitations in complex scenar- ios like plant imagery, which features irregular structures and diverse textures that are significantly different from the current dataset.
Datasets for Reference- based Image Super-Resolutiin SISR datasets such as DIV2K Timofte et al. (207)) and Flickr2K Lim et al. (2017) have been widely adopted to Iearn mappings from LR to HR images. However, they are not well-suited for RefSR tasks, as they lack external Ref images that are crucial for guiding texture enhancement Zheng al. (2018) ; Zhang et al. (2019) ). To bridge this gap, CUFEDS Zhang et al. (2019) is proposed as an early dataset tailored for RefSR, offering 13,761 training pairs of size 160x x 160, each consisting of a HR image and corresponding Ref im- age. CUFEDS provides 126 test groups for evaluation, where each group includes an HR image and four Ref images with varying degrees of similarity. Following this, Lin et al. Zhang al. (2023) introduced the LMR dataset, large-scale multi-reference benchmark designed to further enrich RefSR research. It comprises 112, 142 training groups of 300x300 images, each paired with five Ref images across different similarity levels. The testing set includes 142 groups, where each target im- age is matched with 2 to Ref images. To address the limited diversity of CUFED5, DReiSR Zhou et al. (2025) extends this dataset by constructing more varied training set comprising 13, 7,161 image pairs. In addition, other efforts, such as Sun80 Sun & Hays (001)) and WR-SR Jiang et al. (2021) offer testing scenarios more aligned with real-world applications. Sun80 provides 80 natural images with internet-soureee reference sets, while WR-SR contains 100 image pairs featuring references re- trieved through web search, encompassing diverse visual categories such as animals and landmarks.
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Color
Scale
Rotation
Deformation Background
Ref
HR
Ref
HR
Rel
HR
Ref
(a) Illustration our PlantRSR dataset
(b) Examples of manually unnotated patches for traiming
Figure 2: Category overview and training set construction our PlantRSR dataset.
Despite recent progress, existing RefSR datasets like CUFEDS and LMR offer limited diversity and ill-suited for plant phenotyping- Although DRefSR expands the variety of scenes and includes some plant imagery, the quantity and diversity of plant species remain insufficient (Detailed analy- sis in Appendix C). Plant images pose unique challenges such as large shape deformations, subtle textures, and frequent defocus, which are not well represented in current datasets.
PLANTRSR DATASET CONSTRUCTION
The widely used CUFEDS dataset is constructed from CUFED Wang et al. (2016a) and primarily targets human daily life scenes. Similarly, the LMR dataset, built upon MegaDepth Li & Snavely 22018), focuses mainly on landmark scenarios. These datasets are not well-suited for plant scenes. Furthermore, existing plant-specific datasets remain scarce, making it challenging obtain valid HR-Ref image pairs. To meet the needs of current RefSR tasks, we meticulously captured 6, 6,134 im- age pairs with rich textures using DSLR cameras. The images cover resolutions between 2K and 8K, over 80% of which are above 4K. To ensure the diversity and realism of the dataset, we deliberately captured images that reflect five major types of variations commonly encountered in real-world plant photography: color variations (11,4%), scale differences (18.6%) rotations (27.0%), deformations (30.5%), and background changes (12.5%). These variations are introduced by adjusting camera angles, distances, and viewpoints during data collection. Fig. 2 category, illustrating the challenges posed by different reference conditions in the plant domain.
(a) provides visual examples of each
In SISR, it is common to crop patches from HR images for training randomly. However, in RefSR, corresponding regions with similar content must be cropped from both the HR and Ref images. For example, CUFED5 first randomly crops 160x 160 patches from HR images and then selects corre- sponding patches from Ref images based on predefined correspondence. Similarly, LMR randomly selects 300x patch from the HR image, projects its center to sparse 3D point cloud, and retrieves Ref patches centered at nearby keypoints. However, these automated cropping strategies not suitable for plant images. Due to the frequent presence of defocused backgrounds and com- plex structures in plant scenes, random or keypoint-based cropping often results in uninformative or mismatched patches. Moreover, the significant variations in our paired plant images, such color, scale, and deformation differences, make accurate automatic correspondence challenging. There- fore, we adopt a manual annotation strategy to carefully select semantically matched regions from image pairs, as illustrated in Fig. (b). Then, we obtain 16,585 patch pairs, which are rescaled into two versions (160x 160 and 300x (00x300) to construct the training set (More samples in Appendix K).
4 OUR METHOD
The overview of our method is shown in Fig. 3 (a). Given the LR image I and the Ref image I [ee we aim at generating a high-quality image s that possesses texture-rich details conditioned on Ree For correspondence matching, we follow C2-Matching Jiang al. (202)) and adopt
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(o) Architecture
Ptur
4x Bicubic Upsampling
DeformConv
SKRM
Solective Key-Region Matching
DeformCons
TGDM
Texiure-Guidee Diflision Module
SKRM
P (index map)
DoformCony
Element- Wise Summation
TGDM
TGDM
TGDM
1
(b) Selective Key-Region Matching (SKRM)
Texture-Guided Diffusion Module (TGDM)
Residual Space State Block
Fsol
Selection
Piyy 1ar)
(T-I
Denoising Net (DN)
Selection
DN
dia (1
(index map)
Figure 3: (a) Architecture of our method. (b) Illustration of SKRM. (c) Illustration of TGDM.
contrastive learning He al. (2020) and knowledge distillation Hinton et al. (2015) to train the Ref feature extractor Re and the LR feature extractor LRR. These extractors are used to obtain R (IRE) and ( from the Ref and upsampled LR images, respectively. Afterward, our proposed SKRM selectively matches key regions between v/ Re RRe) and L (-1) by focusing on informative areas, thereby improving matching efficiency and obtaining the correspondence index map P. To utilize both high-level and low-level information provided by Re following previous works Diang et al. (2021); Xia et al. (2022) Cao al. (2022); Zhang al. (2022) extract Ref texture features pRa at different scale levels via encoder Ref Simonyan Zisserman (2015). At
each scale, perform the deformable convolution networks Dai et al. (201)); Zhu al. (2019) on Ref to warp it to match the PLR for later fusion. Finally, our proposed TGDM leverages the diffusion process conditioned on F progressively enhance FLR, thereby enabling more effective multi-scale feature fusion and producing the final SR image ssR We employ the LI loss function as the optimization objective during model training.
4.1 SELECTIVE KEY-REGION MATCHING (SKRM)
Since plant images often contain detailed foregrounds and shallow DoF-induced blurred back- grounds that are easy to reconstruct, explicit matching in such regions is unnecessary. Instead, illustrated in Fig. (b), focus selecting and matching key textured regions. To identify these regions, we introduce a key detail selection indicator. Given a feature map F E R X and sampling ratio 2, the key detail selection metric Mr I Hx (values 0 or obtained through the operation S(-), defined as the absolute difference between the original feature map and its reconstruction via bilinear downsampling and upsampling as:
Mr = S(F) = =T = F
Flsts 1>
(1)
where 7 the mean plus standard deviation of the absolute differences, I(-) returns 1 when the input exceeds threshold т, and 0 otherwise. Considering that the Ref image may also contain blurred background regions, we apply key region selection to the Ref image to enhance matching efficiency with the LR image. Based Eq. [ the key detail selection indicators are derived follows:
Mnef, MLR S(ynes (Ree))) (()L))(TRR))).
(2)
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Subsequently, the key texture features F key and FLR are then derived from these indicators as:
F Fhey Ref - MRaS 1o Re (R))),
(3)
F pkey = MLR LL 1
(4)
Afterward, we get descriptors dRes = Iki..... km] and d= Ig...... qn) from Fkey Rey and F respectively. These descriptors are obtained by folding extracted features into patches. Then we find the patch from the patches ky that is most similar each LR patch qi as:
K |kyl
P. = Topl(
(5)
11
where (-) a function and return the most relevant positions P..
Then, we use a deformable convolution DConw(+) and the position index map to match the similar textures around every position P; of FLR as:
FRff matched,1
DConv (F.ef P.)) Σ WF Ros (P + Po + Am)
(6)
where F:Res( (1
1,2,4) denotes Ref texture features at different scale via encoder &R
1), (1) denotes the convolution kernel weight, Ap and Am denote the learnable offset and modulation scalar, respectively.
4.2 TEXTURE-GUIDED DIFFUSION MODULE (TGDM)
As shown Fig. (c). TGDM enhances the LR feature F:L.R by leveraging the matched Ref tex- ture pftef Fmstched. as conditional guidance within a diffusion- refinement framework, enabling a progressive refinement of the LR features with Ref guidance.
We denote the initial LR feature P.R : Zr, which serves the starting point for the reverse diffusion process. To recover Zu from Zr, we adopt a conditional denoising Ho et al. (2020); Li et al. (2024b). At each timestep t E T 1, ... 1), the Denoising Network (DN) predicts the noise éØ (Z,, f-Ref "matched,) Z, conditioned on the matched Ref texture, and each sampling step can be expressed as:
q 1 - 1o, Zi) NN io (o Zr)3,I), 1
20
Ze - 101 Ct. én Ee,, F Ras .matched,))
(8)
Vor
104-1881 21 + Vail ā(-1)) Zer
14 (Zo, 2,)
1 - 04
āi
3 ā;-1 3r,
(10)
where, or and Br are the forward process coefficients, and 0e = IT' 0t.- This denoising step is iteratively applied from = T to i = 1, producing the refined latent Zo guided by the Ref texture. To further enhance textures feature, we pass Zo through a Residual State Space Block (RSSB) Guo al. (2024) as:
Zo = (RSS((oo)
(1I)
The refined latent Zo is then fused with the original LR feature via residual addition and subse- quently upsampled using a sub-pixel convolution layer (Shi et al. (2016) as:
F upsample(F Zo), 1 1,2.
(12)
Finally, the enhanced feature FLR is used to generate the super-resolved representation by an addi- tional residual connection as:
FSR FLR + Zo-
(13)
TGDM progressively injects Ref-guided textures into the LR representation through the conditional diffusion mechanism, improving feature quality for high-fidelity SR reconstruction.

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5 EXPERIMENTS
5.1 DATASETS AND IMPLEMENTATION DETAILS
Due to the unavailability of the LMR dataset, we employ CUFED5, DRefSR, and our PlantRSR training datasets for comparative evaluation. All models are trained on 160x 160 patches under identical settings to CUFEDS for consistency. The LR images are produced by 4x bicubic down- scaling of HR images. All SR results are evaluated using four metrics: PSNR, SSIM Wang et al. (2004), LPIPS Zhang et al. (0188), and DISTS Ding et al. (020). The PSNR and SSIM values calculated on the Y channel in YCbCr color space. Besides,
introduce M-PSNR, a variant that measures PSNR only in textured regions using a mask similar to Eq. [ In our method, the number of T is set to 4. We train our model with 400 epochs using ADAM optimizer (A1 =0.9 and 8z=0.999). The initial learning rate is set to 10- and decreased by 0.5 after each 100, 000 iteration. We implement experiments using the NVIDIA RTX A6000 GPU.
5.2 QUANTITATIVE EVALUATION
We compare our method with several SOTA RefSR approaches, including MASA Lu et al. -MaMahhning Jiang et al. (021)) AMSA Xia et al. (2022). DATSR Cao et al. (0022). RRSR Zhang al. (2022), MRefSR Zhang et al. (2023), HiTSR Aslahishahri al. (2024). MCMSR Zheng al.
2021
(2024) and SSMTF Zhou et al. (2025). All comparison methods are trained independently on both the CUFED5 dataset and our PIantRSR dataset. The detailed experimental results are listed in Tab Our method achieves the best performance across all four metrics on all training datasets. Further- more, experimental observations reveal that models trained on PlantRSR consistently outperform those trained on CUFEDS and DReASR. Notably, Ours achieves superior performance with only 11.1M parameters, significantly fewer than competitors like RRSR (21.5M) and MRefSR (23.7M), Moreover, all methods exhibit consistent performance improvements when trained on PlantRSR compared to both CUFEDS and DRefSR. Besides, the significantly greater improvement in M- PSNR compared to standard PSNR demonstrates our dataset's enhanced effectiveness for complex plant textures. Experimental results validate method superiority and PlantRSR dataset significance.
5.3 QUALITATIVE EVALUATION
We visually compare our method and other methods in Fig. In Fig. (a-c), we present the visual comparison of plant leaf reconstruction. The restoration of leaf venation patterns proves particularly challenging, where our method demonstrates superior texture recovery compared to other approaches. Fig- (d) and (e) present two challenging scenarios involving both color and scale variations. Despite these difficulties, our method achieves superior reconstruction quality compared other compared approaches. In the complex botanical texture case shown in Fig, 4 (f), our method achieves superior fidelity, while competing methods suffer from noticeable blurring effects. The visual results demonstrate the superiority of our method across various challenging in plant image.
5.4 ABLATION STUDY
About our SKRM. We conduct an ablation study by comparing our SKRM with existing efficient matching methods, including Match & Extraction Module (MEM) Lu et al. (202]) and Coarse-to- Fine Embedded PatchMatch (CFE-PatchMatch) Xia al. (202)) We calculate GFLOPs for an LR image (400 199) and Ref image (1200x 796). As listed in Tab. 2 our SKRM achieves the lowest computational cost (77.86 GFLOPs) while maintaining the best performance. Compared to the exhaustive Enumerated Matching method, SKRM significantly reduces the computation by over demonstrate the efficiency of our SKRM.
150x. These results (More results Appendix G
We conduct ablation study by comparing our proposed TGDM with exist- ing texture fusion methods, including Dynamic Aggregation (DA) Jiang et al. (2221) and Residual Feature Aggregation (RFA) Cao et al. (2022). 2) As listed in Tab. 3 TGDM achieves the highest performance, outperforming both DA and RFA. To further assess the contribution of the diffusion mechanism in TGDM, we also compare it with variant that removes the diffusion module. The result shows a performance drop from 38.62 to 38.52 in PSNR, highlighting the importance of the diffusion design. These results validate the effectiveness of our TGDM.
About our TGDM.
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Quantitative comparisons of RefSR methods, each independently trained on the CUFED5 Zhang et al. (219)) DRefSR Zhou et al. (2025). and our PlantRSR dataset, with eval- uation performed on PlantRSR testing sets. Bold text indicates the best results achieved on the respective training datasets.
Table 1:
Method
Param.
Training set
PSNR
M-PSRN1
SSIMI
LPIPS!
DISTS!
CUFED5
38.00
30.46
0.9491
0.1344
0.0863
MASA
4.0M
DReiSR
38.06
30.54
0,9495
0.1338
0.0859
PlantRSR
38.24
30.80
0,9510
0.1323
0.0852
CUFEDS
38.23
30.81
0,9508
0.1327
0.0857
- -MMathhin
8.9M
DRe[SR
38.27
30.87
0.9509
0.1323
0,0855
PlantRSR
38.43
31.20
0.9523
0.1300
0.0851
CUFEDS
38.24
30.83
0,9509
0.1324
0.0851
AMSA
9.7M
DRefSR
38.29
30.89
0.9511
0.1321
0.0849
PlantRSR
38.43
31.23
0.9526
0.1303
0.0842
CUFEDS
38.25
30.81
0.9512
0,1316
0,0842
DATSR
18.0M
DRe[SR
38.32
30.91
0,9515
0.1312
0.0839
PlantRSR
38.48
31.26
0.9527
0.1304
0.0835
CUFEDS
38.26
30.83
0,9510
0.1307
0.0838
RRSR
21.5M
DRefSR
38.31
30.93
0.9513
0.1303
0.0835
PlantRSR
38.44
31.22
0.9524
0.1299
0.0829
CUFEDS
38.21
30.76
0,9502
0.1334
0.0862
MRefSR
23.7M
DRefSR
38.30
30.91
0.9511
0.1328
0.0859
PlantRSR
38.42
31.15
0.9516
0.1317
0.0853
CUFEDS
37.84
30.12
0.9482
0.1399
0.0911
HiTSR
13.7M
DRefSR
37.90
30.21
0,9487
0,1390
0.0907
PlantRSR
38.07
30.49
0.9499
0.1371
0,0900
CUFED5
38.26
30.82
0.9510
0.1322
0.0851
MCMSR
8.9M
DReiSR
38.30
30.90
0,9516
0.1317
0.0849
PlantRSR
38.43
31.23
0.9526
0.1302
0.0844
CUFEDS
38.28
30.86
0.9514
0.1305
0.0834
SSMTF
13.9M
DRefSR
38.31
30.92
0.9517
0,1303
0.0833
PlantRSR
38.49
31.25
0.9528
0.1297
0.0831
CUFED5
38.40
31.14
0,9522
0.1294
0.0828
Ours
11.IM
DRefSR
38.49
31.27
0.9529
0.1292
0.0828
PIantRSR
38.62
31.53
0.9538
0.1288
0.0826
Table Ablation study of our SKRM.
Table 3: Ablation study of our TGDM.
Matching Method
GFLOPs
PSNR/SSIM
Fusion Method
PSNR/SSIM/M-PSNE
MEM 620.72
38.36/0.9517
DA RFA
0113/0.95133311
CFE-PatchMatch
124.73
38.58/0.9534
38.48/0. 03.48/0.9521311.22
Enumerated Matching
11990.39
38.62/0.9542
TGDM (w/o diffusion)
38.52/0.9531/31
SKRM
77.86
38.62/0.9538
TGDM
38.62/0.9538/11.5
About our PlantRSR dataset. To validate the effectiveness of our PlantRSR dataset, we train method CUFEDS, DRefSR, and PlantRSR datasets, respectively, and evaluate the models PlantRSR testing dataset. As shown in Fig. our model trained on the PlantRSR dataset consistently outperformed the models trained on CUFEDS and DRefSR across the entire training process, with the highest PSNR of 38.62 dB compared to 38.40 dB and 38.50 dB. This quantitative improvement demonstrates that our PlantRSR dataset significantly outperforms existing CUFED5 and DRefSR datasets when handling complex botanical textures. Besides, shown in Fig. 6 (a), the model trained on the PlantRSR dataset better leverages Ref image to recover fine textures, such as plant trichomes. Fig. 6 (b) exhibits that dataset achieves superior recovery of irregular floral textures. These results substantiate the effectiveness of our PlantRSR dataset.
About comparisons of other testing datasets. further demonstrate the effectiveness our method, conduct comparative experiments on the CUFEDS and WR-SR testing datasets com- monly compared RefSR task (See Appendix [ for other datasets). All methods are trained on CUFED5 datasets. As listed in Tab. 4 our method consistently outperforms all competitors across both datasets, achieving superior results (See Appendix for visual comparison).
About sampling steps. We investigate the impact of sampling steps T in our method. As shown in Fig. 7 increasing the number of steps improves the performance, with PSNR gradually saturating after T 4. We also evaluate different texture types by grouping the test images according their
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Trained with DRefSR
Trained with our PlantRSR
37, 23 10.932/0.155 37.27000544 152 37.130002500
37 9S3IN 144 $7.56/0.00010 196. 37,5400.9000 199
aa) LR Image
RRSR
MRefSR
MCMSR
(d) LR Image
RRSR
MRRfSR
MCMSR
941.0.4
PINR-SSIMILPIIS
Ref Image
SSMTF
Ours
HR
Ref Image
SSMTF
Ours
HR
98.71.9955001199 9570.15
38.0910.952:00120
36.54/0938-11215
36.62 222
36.3600-9P902230
(b) LR Image
RRSR
MReISR
MCMSR
c LR Image
RRSR
MReISR
MCMSR
38.950 964111611111
PSNRISSMMILIPSS
2161-126
PSIN SISMELPIPSS
Ref Image
SSMTF
Ours
HR
Ref Image
SSMTF
Ours
HR
38.1610.82288117
35.780092449.251
35.709..22202999
35.73/0 92300.229
(c) LR Image
RRSR
MRefSR
MCMSR
ff LR Image
RRSR
MRefSR
MCMSR
38.24-8, 11/
38.4668.96558.. 18S.
PSNRUSSIMILPIIS
85/D 85/00211000210 35 S5.0THBLOE 025-0225
PSNNR-SSIMILPPS
Ref Image
SSMTF
Ours
HR
Ref Image
SSMTF
Ours
HR
Figure Visual comparison of SOTA RefSR methods, each independently trained on both the DRefSR dataset and our proposed PlantRSR dataset, with evaluation performed on the PlantRSR.
38.6
38.4
(a) Input
CUFEDS
CUFEDS
PSNR (dB)
38.2
Trained on PlantRSR
38.0
Trained DRefSR
Ref
PlantRSK
HR
PlantRSR
HR
37.8
Trained CUFED5
Best PSNR: 38.62
37.6
Best PSNR: 38.49
Input LR
LR
CUFEDS
LR
CUFEDS
Best PSNR: 38.40
37.4
0
100
200
300
Epoch
Figure 5: Ablation study comparing model performance when trained on the CUFED5, DRefSR, and PlantRSR datasets.
Ref
PlantRSR
HR
PlantRSR
HR
Figure 6:
Visual comparison between models trained on CUFEDS and our PlantRSR dataset.
complexity, such as fine veins (25 images) versus thick stems (37 images). As shown in Tab. 5 across all groups, we observed the same trend as Fig. [ performance steadily improves when increasing 7 and saturates T 4. Importantly, texture category benefits from larger sampling

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Published as a conference paper at ICLR 2026
Table 4: Quantitative comparisons SOTA methods on CUFEDS and WR-SR testing datasets, evaluated using PSNR, SSIM, and LPIPS.
PSNR (dB)
38.4
Method
CUFEDS
WR-SR
on
Trained
RRSR
28.83/0.8563//
000338866604240 2241
08.41/0,8039/00 2
Our best PSNR: 38 38.62
MRefSR 28.63/0 8523/0 2281
28.26/0.
006/008011//// 8012/0 2912
38.2
Our best PSNR: 38.49
HiTSR 27.08/0 8012/0 2960
28.26/0.80117 0001000.2988
MCMSR 28.54/0. 0490009990000222 2294
Sampling Steps
SSMTF 28.86/0 8595/0.22000
28.42/0.8056/0 8056/0.2844
Ours
28.95/0.86021.2116
28.51/0.8062/0.2813
Figure 7: Ablation study of T in TGDM.
steps beyond 7 4, confirming that the diffusion refinement behaves consistently across different
plant textures. Based on the trade-off between performance and efficiency, we T 4.
Table 5: About the effects of different sampling steps different plant textures.
Texture Type
T=1
T=2
T=3
T=4
T=6
T=8
Fine Veins
27.62
27.88
27.95
28.04
28.06
28.07
Thick Stems
36.82
37.07
37.26
37.32
37.33
37.34
About plant categories. We provide clearer description of plant categories, growth-stage diversity, and real-scene complexity. The dataset covers four major categories of commonly encountered vegetation: crops, wild plants, ornamental plants, and aquatic plants. To improve generalization, data acquisition was conducted over more than one year, covering spring, summer, and autumn, thus naturally capturing early leaf expansion, mid-growth, and mature stages. The overall distribution is listed in the Tab.
Table 6: About category of our PlantRSR.
Table 7: About environment of our PlantRSR.
Category
Percentage
Growth-Stage
Environmental Factor
Percentage
Crops 10.2%
early, mid, mature early, mid, mature early, mid, mature mid, mature
Illumination variations
22.7%
Wild plants
36.3%
Weather effects
9.8%
Ornamental Plants
45.3%
Background clutter
34.6%
Aquatic Plants
8.2%
Leaf damage
7.3%
About collection environment. The dataset was deliberately constructed to encompass wide spec- trum of natural environmental changes. Images were captured across different seasons include diverse factors such as illumination variations, weather effects, background clutter, and instances of leaf damage. A quantitative summary these conditions and their approximate proportions within the dataset is provided in Tab. 7
CONCLUSION
In this paper, we address the limitations of existing RefSR datasets and methods in handling natu- ral scenes with complex textures, particularly plant imagery. We introduce PlantRSR, a large-scale RefSR dataset containing 16,585 high-quality HR-Ref pairs that capture the diverse and fine-grained characteristics of botanical scenes. To fully exploit this dataset, we propose a novel ReiSR frame- work fcaturing two key components: SKRM selectively matches botanical textures enhance efficiency, and a TGDM for progressive refinement of LR features using Ref-guided diffusion. Ex- tensive experiments demonstrate that method outperforms existing SOTA approaches in both quantitative metrics and visual quality, highlighting the effectiveness of our design and the value of the PIantRSR dataset for advancing RefSR research in natural and fine-grained scenarios.
7 ACKNOWLEDCEMENTS
The research is supported by National Science and Technology Major Project 1022200011204), National Science Fund for Distinguished Young Scholars (62125601), and National Natural Science Foundation of China (62576031).
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A THE USE OF LARGE LANGUAGE MODELS
We used Large Language Models to assist or polish the writing, without involving our experiments, figures, or other core contributions.
B ARCHITECTURE OF THE DENOISING NETWORK (DN)
As shown in Fig. DN is designed to restore the latent feature Zo by leveraging both Ref texture features and diffusion timestep. Given the diffusion timestep Embedder maps it into an embedding femh E IR.. This embedding then fused with the matched Ref features R-Ref matched via a simple addition, and passed through SiLU activation followed by linear transformation to yield three modulation vectors a, b, c E Rdd
F
Bef natched
Embedder
SILU
Linear
femb Embedder(t))
(14)
x3
a, b, Linear( (SiLU(Ree matched + femb))
Ref
(15)
AdaL
Conv
ReLU
Conv
The input latent Ze is passed by the adaptive layer normalization (AdaLN) Perez et al. (2018); Li et al. (202bb). The result is adaptively modulated using the scale vector a and shift vector b:
Z..
Denoising Network (DN)
Figure 8: Architecture of Denoising Net.
Ft. = a X Adal 7x) + b.
(16)
This modulated feature da is further refined by a ResBlock He et al. 2016), whose output is scaled by and added back to the original input Z, to obtain the Z,
Z & Res Block(x+) + Zt.
(17)
To enhance the representational capacity and further inject Ref information into the denoising pro- cess, the above procedure is repeated three times within the DN. This recursive structure enables more effective integration of Ref textures and temporal priors for progressive feature refinement.
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C COMPARISON WITH THE DREESR DATASET
Although DReiSR contains some plant scenes, its limited scale and diversity are insufficient to address the challenges in plant imagery. As shown in Tab, g DRetSR comprises only 1,400 im- ages, whereas our PlantRSR dataset offers 6,134 high-resolution images, with 80% exceeding 4K resolution and exhibiting superior visual quality. Furthermore, PlantRSR provides hensive coverage across five key categories, while DRefSR contains merely a few dozen images in critical dimensions such as color variation, scale diversity, and background complexity far below the requirements for robust model training. The experimental results in Tab. and Fig further demonstrate the superior effectiveness of our PlantRSR dataset.
compre-
Table 8: Analysis of plant image diversity and number in DReASR and our PlantRSR.
Category
Color
Scale
Rotation
Deformation
Background
All
DReiSR
(3.7%)
83 (5.9%)
332 (23.7% 7%)
884 (63.2%)
49 (3.5%)
1,400 6,134
PlantRSR 697 (11.4%)
1,142 (18.6%)
1,657 (27.0%)
1,872 (30.5%)
766 (12.5%
D COMPARISON WITH SOTA METHODS ON OTHER TESTING DATASETS
To further demonstrate the effectiveness of our method, we conduct comparative experiments on the Sun80 Sun & Hays 2012) and DReiSR Zhou et al. (2025) testing datasets commonly compared in RefSR task. For fair comparison, all methods are trained on the CUFEDS dataset. As presented in Tab. our method surpasses all competing approaches across all evaluation metrics on both datasets, demonstrating its consistent superiority and effectiveness.
Table 9: Quantitative comparisons of SOTA methods Sun80 Sun & Hays 2012) and DRefSR Zhou et al. (2025) testing datasets, evaluated using PSNR, SSIM, and LPIPS.
Method
Sun80
DRefSR
RRSR
30.13/0 1/3300816//.. 816/0.303
$1.69/0.867/0.1 260
MRefSR
30.28/0.88 011/00081900..000
31.72/0.868/0.22 868/0.279
HiTSR
30.24/0.82 820.0.293
01.26/0.858/0.22
MCMSR
30.21/0 818/0.300
31.24/0. 55660.2944
SSMTF
30.38/0.824/0. 826.
0015/0.869/0222
Ours
30.41/0.824/0.288
31.77/0.871/0.272
E VISUAL COMPARISON ON OTHER TESTING DATASETS
To further demonstrate the effectiveness of our method, we conduct comparative experiments on the CUFEDS Zhang et al. (2019) and WR-SR Jiang et al. (202I) testing datasets commonly compared in RefSR task. All methods are trained CUFEDS datasets. As shown in Fig. II method demonstrates superior visual reconstruction performance compared to other approaches across both datasets.
Table 10: Performance in terms of different similarity levels on CUFEDS dataset. All methods
trained on CUFEDS dataset.
Similarity Levels
DATSR
RRSR
HiTSR
MCMSR
SSMTF
Ours
L1
28.50/0.850
28.63/0.851
26.82/0.797
28.54/0.849
28.76/0.854
28.86/0.855
L2
27.47/0.820
27.67/0.821
26.68/0.785
27.54/0.808
27.71/0.824
27.81/0.826
L3
27.22/0.811
27.41/0.813
26.56/0.783
27.27/0.810
27.46/0.816
27.58/0.819
L4
26.96/0.803
27.15/0.804
26.43/0.781
27.03/0.801
27.19/0.807
27.29/0.809
LR
25.75/0.754
26.53/0.784
26.53/0.782
26.41/0.782
26.68/0.791
26.70/0.791
Average
27.18/0.808
27.47/0.815
26.60/0.786
27.36/0.810
27.56/0.818
27.65/0.820
F ABOUT SIMILARITY OF REF IMAGE
To evaluate the impact of reference images with varying similarity levels on model performance, which provides four Ref images (I.1-14) with decreasing similarity to the LR image, where I. is
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the most similar and L4 the least. We evaluate method using all similarity levels, including the LR image itself as a Ref when similar ones are unavailable. As listed Tab. [0] our method consistently outperforms SOTA approaches across all levels, even when using only the LR image as a Ref, demonstrating its robustness and generalization ability.
Table 11: Computational cost and memory usage of different methods.
Method
Param.
Runtime
Memory
RRSR
21.5M
1.496s
13.3G
MReiSR
23.7M
0.774s
14.1G
HiTSR
13.7M
0.843s
8.5G
MCMSR
8.9M
0,681s
8.6G
SSMTF
13.9M
1,435s
15,2G
Ours
11.1M
1.116s
11.4G
G COMPUTATIONAL COST AND MEMORY USAGE
To assess the practicality of our method, compare the runtime and memory consumption with several recent ReISR methods in Tab. I All values are measured using an LR image of size x00x200 and Ref image of size x200 800. As listed in Tab. [1] while our method does not achieve the lowest runtime or memory usage, it maintains reasonable balance between efficiency and performance. Although we adopt a diffusion process, the network structure lightweight, consisting of only three ResBlocks, and the sampling steps during inference are limited to four. Therefore, the computational and time costs are not as significant. In addition, our SKRM module effectively reduces the time required for Ref texture matching, resulting in competitive overall runtime and memory usage.
Table 12: Comparison with diffusion- methods on PlantRSR dataset.
Method
PSNR
SSIM
L.IPS
DISTS
SinSR Wang ct al 2024b
31.60
0.8580
0.1886
0.1449
DoSSR Cui et al StableSR OSEDim Wu 2024
2024
30.82
0,8600
0,2192
0,2091
Wang
31.21
0.8774
0.2011
0.1930
32.22
0.8975
0.1602
0.1312
Ours
38.62
0.9538
0.1288
0.0826
H COMPARISON WITH DIFFUSION-BASER METHODS
We conduct comparative experiments with diffusion based methods, as listed Tab, 12 should be noted that this comparison may present certain inequities and is primarily intended for experimental reference. These diffusion- based approaches are built upon generative diffusion models, while our method is trained using pixel-wise losses (e.g.. LI loss). Furthermore, the diffusion architectures substantially larger and more complex, whereas our method adopts a lightweight design comprising only three ResBlocks. Additionally, these comparative methods do not utilize reference images, which places them at a inherent disadvantage in the reference-based super-resolution setting.
Table 13: The user study. Compared to other methods, over 90% users prefer our results.
Ours vs. RRSR
Ours vs. MRetSR
Ours vs. MCMSR
Ours vs SSMTF
93%
95.3%
93%
90.7%
USER STUDY
To further validate the qualitative superiority, as listed in the table below, we conduct user study involving 43 participants to compare the visual quality of our method against several methods PlantRSR dataset, including RRSR, MRefSR, HiTSR, MCMSR, and SSMTF. each comparison, participants shown image pairs, with generated by Ours, and asked to select the image
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with better visual quality. According the Tab. 13 over 90% of participants favor the results of our method over the other approaches.
J LIMITATION
While this work advances RefSR for plant imagery, two key limitations should be noted. First, hardware constraints force suboptimal downsampling of our high-resolution dataset (80% > 4K, range 2K-8K); preventing full utilization the available image detail. Second, performance de- grades when processing dissimilar Ref images, the current matching mechanism lacks adaptabil- ity to low-similarity scenarios. These limitations point to important research directions: developing memory-efficient architectures for altra-high-resoouuiin processing and designing more selective Ref utilization strategies. Addressing these challenges would significantly enhance the practical applicability of plant image SR.
K PARTIAL SAMPLES FROM THE PLANTRSR DATASET
As shown in Fig. and Fig. our PlantRSR dataset contains meticulously annotated samples spanning multiple plant categories with rich textures. The manual annotation process specifically captures challenging plant scenarios, ensuring dataset quality for advanced botanical studies.
L ETHICS STATEMENT
Our proposed dataset focuses on plant scene imagery, and our method is designed for reference- based image super-resolution. Neither the dataset nor the proposed method involves human subjects, personal data, or sensitive information. The dataset was constructed from images captured by our team and does not infringe upon the interests of any individuals communities. All experimental results are derived from either publicly available datasets or our constructed dataset. We do not foresee any direct social harms arising from this research.
M REPRODUCIBILITY STATEMENT
The generation pro- cess of our PlantRSK dataset presented in Section 3] The training details of our proposed method described in Section 5.1 of the main text. More details of denoising network are given in Ap- pendix B while (rpendix[Kprovidee more samples from the PlantRSR training and testing datasets.
Our codes released at: https:// hwtp:ttwww..ccm/cccc/aaaaaaatata
The usage of large language models is discussed in Appendix A
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Figure 9: Partial Samples of PlantRSR training dataset.
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Figure 10: Partial Samples of our PlantRSR testing dataset.
Testing on CUFEDS dataset
Testing on WR-SR dataset
(a) LR Image
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(c) LR Image
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(b) LR Image
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Figure Visual comparison with SOTA methods on CUFEDS and WR-SR datasets.
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Reviews:
[{"review_id": "86JOYU2aoh", "reviewer": "Reviewer_rAhJ", "summary": "The paper introduces PlantRSR, a large-scale dataset for reference-based super-resolution (RefSR) in plant imagery, containing high-resolution (HR) images paired with high-quality reference (Ref) images that capture the complexity of real-world plant scenes. Building on this dataset, the paper proposes a RefSR method leveraging a Selective Key-Region Matching (SKRM) module for efficient region-wise alignment between low-resolution (LR) and Ref images, and a Texture-Guided Diffusion Module (TGDM) that progressively refines LR features under reference-guided diffusion. Extensive experiments on PlantRSR and other RefSR benchmarks show state-of-the-art performance in visual metrics of the proposed method.", "questions": "1. As the zoomed views in Figure 4 indicate, the proposed method achieves superior fidelity. Can this advantage be captured quantitatively by metrics?\n2. In the PlantRSR dataset, are there specific categories of plants or scenes that are particularly challenging for RefSR?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper has a strong motivation and innovatively proposes the PlantRSR dataset, which presents high-fidelity imagery that captures the complexity and variability of real-world plant scenes. This dataset fills some gaps in the current RefSR datasets, which often lack diversity and realism in plant imagery.\n2. The proposed SKRM module is effective, yielding the strongest overall performance; more importantly, it achieves the lowest computational cost among matching methods, making it both accurate and efficient.\n3. The paper is well-organized and easy to understand.", "weaknesses": "1. The methodological novelty appears limited. The core two modules, SKRM and TGDM, bear resemblance to prior RefSR methods (region/patch matching and diffusion-based refinement). The changes seem incremental and not clearly differentiated by a new principle or theory. As currently presented, the modifications do not appear substantial enough to support a strong claim of innovation.\n2. In Table 1, although the set of compared methods is fairly comprehensive, the improvements are quite limited. It is suggested to increase the super-resolution scale or introduce more discriminative metrics to better demonstrate the proposed method’s advantages. The ablation study in Table 3 exhibits similar issues.\n3. The literature review is insufficient. The related work should include a more comprehensive discussion of recent RefSR methods based on diffusion models.\n4. Please review the manuscript to avoid typo errors, for instance:(1) In table 4, \"SOAT\" should be corrected to \"SOTA\"; (2) In table 9, \"LPISP\" should be corrected to \"LPIPS\"; (3) In Figure 4, \"PRefSR\" should be corrected to \"PlantRSR\". (4) \"SSMFT\" should be corrected to \"SSMTF\".", "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": "AjDFyz2lFO", "reviewer": "Reviewer_tpSi", "summary": "This paper introduces PlantRSR, a large-scale reference-based super-resolution dataset specifically designed for plant images, and proposes a RefSR method that integrates Selective Key-Region Matching (SKRM) and a Texture-Guided Diffusion Module (TGDM). The proposed approach achieves state-of-the-art performance on the PlantRSR dataset as well as on other benchmark datasets.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "The PlantRSR dataset effectively addresses the lack of plant-specific scenes in current RefSR tasks, providing a high-quality and diverse supplement.\n\nThe manually constructed aligned patches successfully mitigate inaccurate alignment issues caused by automatic cropping in existing works, improving training quality.\n\nThe paper is well-organized with clear formatting and includes comprehensive figures and tables.", "weaknesses": "1.The evaluation datasets are somewhat limited. Beyond CUFED5 and PlantRSR, it is recommended to incorporate datasets from real retrieval scenarios (e.g., WR-SR or Sun80) to assess robustness under low-relevance reference images.\n\n2.Diffusion-based approaches typically incur large computational overhead. Reporting inference time and memory consumption would help assess practical usability.\n\n3.The paper lacks sensitivity analysis on sampling ratios or patch granularity, making the influence on generalization unclear.\n\n4.Selective Key-Region Matching is conceptually similar to previous methods such as MASA.\n\n5.Comparisons with diffusion-based large models (e.g., SinSR, DoSSR) are missing.\n\n6.The details of texture injection in TGDM are insufficiently explained.", "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": "n9eW0kmYqi", "reviewer": "Reviewer_gwnq", "summary": "Aiming at the problems that the existing reference super-resolution (REFRSR) data sets lack the coverage of plant scenes and the existing methods are difficult to deal with the complex texture of plants, this paper proposes a large-scale plant-specific REFRSR data set PlantRSR and the corresponding REFRSR method. PlantRSR contains 16,585 manually labeled HR-Ref image pairs, covering five real scene variations, such as color, scale and rotation, with a resolution range of 2K-8K, which fills the gap in the RefSR data set of plant scenes. The proposed method includes two core modules: selective key region matching (SKRM), which focuses on the key texture regions of plants to improve the matching efficiency; Texture-guided diffusion module (TGDM) refines low-resolution features through diffusion process with reference texture as the condition. Experiments show that the PSNR, SSIM and other indicators of this method are better than the existing SOTA method on data sets such as Planter SR and CUFED5, and the parameter quantity (11.1M) is more advantageous. More than 90% participants in user research prefer its visual results.", "questions": "Please refer to weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.A large-scale RefSR data set specially for plant scenes is constructed, which solves the limitation of existing data sets focusing on human activities and architectural scenes. Manual annotation is used to generate semantically aligned HR-Ref patch pairs, which avoids the mismatch problem caused by automatic clipping, and covers a variety of real scene variations and meets the practical application requirements such as plant phenotype analysis.\n2.It covers data set validity verification (comparing the model performance of different training sets), module ablation experiment (the individual contribution of SKRM/TGDM), cross-data set flooding test (CUFED5, WR-SR), diffusion method comparison and user research, and proves the superiority of the method and data set in multiple dimensions.\n3.The parameters of the method are only 11.1M, which is much lower than those of RRSR(21.5M), MRefSR(23.7M) and other competitors, and the inference sampling step is only 4 steps, which balances the performance and deployment efficiency.", "weaknesses": "1. The specific coverage of plant species (including crops, wild plants and other different categories) and the differences in growth stages are not clearly stated, which may affect the generalization of the model to special plant types. The collection environment does not mention complex real scenes such as light change, pest pollution and so on, so the robustness of the data set needs to be supplemented.\n2. The selection of key areas of the existing SKRM module depends on the texture difference threshold, and lacks the adaptive adjustment mechanism for low similarity scenes.\n3. The diffusion sampling step (T=4) of TGDM is only determined by the performance saturation curve, and the effects of different sampling steps on different plant textures (such as fine veins and thick stems) are not analyzed.\n4. The resolution of 80% images in PlantRSR is higher than 4K, but it is down-sampled due to hardware limitations, which fails to give full play to the detail value of ultra-high resolution images.", "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": "JGWzc14dFQ", "reviewer": "Reviewer_VMqn", "summary": "This paper presents a new reference-based super-resolution (RefSR) method for plant imagery. Potential contributions are threefold:\n(1) a large-scale PlantSR dataset for plant imagery,\n(2) a Selective Key-Region Matching (SKRM) for efficient LR-Ref correspondence,\nand (3) a Texture-Guided Diffusion Module (TGDM) that leverages a reference-texture-conditioned diffusion process for feature enhancement.\nThe method is evaluated on the new PlantRSR and existing general RefSR datasets, demonstrating competitive performance.", "questions": "**(1) Dataset Impact:** Beyond a new benchmark, can the authors demonstrate the practical impact of the proposed PlantSR dataset? For instance, does using the proposed RefSR method on this data lead to improved performance on a downstream task like plant species classification or disease detection?\n\n**(2) Generalization:** Does training on this plant-specific dataset produce any generalizable knowledge? Please consider reporting results of a model (pre-trained or fine-tuned on PlantSR) evaluated on a standard, non-plant RefSR benchmark (e.g., CUFED5).\n\n**(3) TGDM Novelty & Ablation:** Please provide an internal ablation study (e.g., removing the RSSB, and modifying the conditioning mechanism). Can the authors provide a runtime/FLOPs analysis to justify the cost-to-benefit ratio of the diffusion step inside TGDM?\n\n**(4) Limitations:** Can the authors provide a more quantitative analysis of this method's limitations?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**Clarity and Reproducibility:**\nThe paper is generally well-written and easy to follow. The inclusion of the source code strengthens the paper's credibility and reproducibility. However, it is important to release both code and data for full reproducibility.\n\n**New Dataset:**\nThe efforts for constructing a large-scale dataset are appreciated. This RefSR dataset for plant imagery may enable new research about, e.g., agricultural and forestrial imaging techniques.\n\n\n**Effective Method Designs:**\nSKRM demonstrates balanced performance-efficiency, as evidenced by comparisons in Table 2 and Table 8 (Appendix). TGDM also shows clear advantages over existing fusion methods in Table 3.", "weaknesses": "**1. Dataset and Method Significance:**\nThe significance of introducing a large-scale and domain-specific dataset requires further establishment.\n\n***(1) Downstream Impact:*** \nDemonstrating downstream applications (e.g., improved plant disease detection accuracy) of plant imagery super-resolution would significantly strengthen the practical value of this work.\n\n\n***(2) Generalization:*** \nAn important question is whether learning to address the challenges of RefSR in plant imagery leads to models to generalize. The authors are suggested to investigate whether a model trained (or fine-tuned) on this PlantSR dataset can achieve improved performance on existing non-plant RefSR datasets. This helps demonstrate a broader value of PlantSR towards developing more powerful RefSR approaches.\n\n\n***(3) Dataset Split Rigor:***\nThe extremely high train-test split ratio (around 60:1) choice requires justification. A discussion on how the 100 test images were selected to represent the dataset's diversity and complexity is necessary for a reliable RefSR evaluation for plant imagery.\n\n\n**2. Technical Novelty of TGDM and its Evaluation:**\nWhile TGDM is claimed as one of the major contributions, its technical novelty is limited, as a combination of existing techniques, including the conditional diffusion process (Ho et al., 2020; Li et al. 2024), the Residual State Space Block (Gun et al., 2024), and the sub-pixel convolution (She et al. 2016).\n\n***(1) Incomplete Ablation Study:***\nThe ablation study in Table 3 is incomplete. To evaluate its internal designs, a detailed ablation within the TGDM is required, including the Residual State Space Block and the sub-pixel convolution. Without this, the module feels like a black box where its true novelty remains unclear.\n\n\n***(2) Marginal Gains vs. Computational Cost:***\nThe performance gain from the diffusion step appears marginal (e.g., +0.1PSNR and +0.0007SSIM). Given the well-known computational overhead of diffusion models, a clear analysis of the performance-efficiency trade-off is necessary to justify this choice. What is the additional inference time/FLOPs? Is there any significant visual improvement?\n\n\n***(3) Unfair Comparisons to Diffusion Baselines***\nThe comparisons with diffusion-based methods in Table 9 (Appendix) are unfair, as those methods are not designed or trained for the RefSR task. A fair evaluation would require modifying and training these baselines with the same RefSR framework (using the reference images and the same losses). Without this, the evaluation on the diffusion step of TGDM is not informative.\n\n\n**3. In-Depth Discussion on Limitation:**\nThe authors acknowledge performance degradations in processing dissimilar LR-Ref pairs, which is appreciated. To strengthen the depth of discussion, the authors are suggested to answer:\n(1) To what extent of dissimilarity would the method fail? Would it be possible to use semantic feature distance to measure the dissimilarity and plot performance (PSNR/SSIM) against this metric? This may provide concrete insight into the operational boundaries of their method.\n\n(2) To what extent of degradation would the method suffer when processing unmatched LR-Ref image pairs? Considering diffusion is used in this method, would it generate new and unwanted patterns?\n\n**Justification for Recommendation** This paper presents a new dataset and a new RefSR method, with promising results. Meanwhile, the broader impact of the dataset and the novelty/necessity of TGDM require a more rigorous establishment. The core issue lies in reframing the contribution from \"a good method for plant image RefSR\" to \"a dataset and method that advances the general understanding and capability of RefSR\".", "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)