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REVIEW 3 major objections 4 minor 70 references

NTIRE 2025 Challenge on Image Super-Resolution (x4): Methods and Results

T0 review · 3 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read The 2025 edition of the classical ×4 super-resolution benchmark sets a new state of the art, with 33.46 dB PSNR for restoration and 4.3472 for perception.

desk verdict A useful but conventional NTIRE challenge report: the restoration numbers are likely solid, yet the headline SOTA claim hinges on a 0.03 dB margin and the perceptual ranking rests on an unvalidated composite metric. read the letter →

arxiv 2504.14582 v3 pith:7ZWOS33B submitted 2025-04-20 cs.CV

Zheng Chen , Kai Liu , Jue Gong , Jingkai Wang , Lei Sun , Zongwei Wu , Radu Timofte , Yulun Zhang
show 103 more authors
Xiangyu Kong Xiaoxuan Yu Hyunhee Park Suejin Han Hakjae Jeon Dafeng Zhang Hyung-Ju Chun Donghun Ryou Inju Ha Bohyung Han Lu Zhao Yuyi Zhang Pengyu Yan Jiawei Hu Pengwei Liu Fengjun Guo Hongyuan Yu Pufan Xu Zhijuan Huang Shuyuan Cui Peng Guo Jiahui Liu Dongkai Zhang Heng Zhang Huiyuan Fu Huadong Ma Yanhui Guo Sisi Tian Xin Liu Jinwen Liang Jie Liu Jie Tang Gangshan Wu Zeyu Xiao Zhuoyuan Li Yinxiang Zhang Wenxuan Cai Vijayalaxmi Ashok Aralikatti Nikhil Akalwadi G Gyaneshwar Rao Chaitra Desai Ramesh Ashok Tabib Uma Mudenagudi Marcos V. Conde Alejandro Merino Bruno Longarela Javier Abad Weijun Yuan Zhan Li Zhanglu Chen Boyang Yao Aagam Jain Milan Kumar Singh Ankit Kumar Shubh Kawa Divyavardhan Singh Anjali Sarvaiya Kishor Upla Raghavendra Ramachandra Chia-Ming Lee Yu-Fan Lin Chih-Chung Hsu Risheek V Hiremath Yashaswini Palani Yuxuan Jiang Qiang Zhu Siyue Teng Fan Zhang Shuyuan Zhu Bing Zeng David Bull Jingwei Liao Yuqing Yang Wenda Shao Junyi Zhao Qisheng Xu Kele Xu Sunder Ali Khowaja Ik Hyun Lee Snehal Singh Tomar Rajarshi Ray Klaus Mueller Sachin Chaudhary Surya Vashisth Akshay Dudhane Praful Hambarde Satya Naryan Tazi Prashant Patil Santosh Kumar Vipparthi Subrahmanyam Murala Bilel Benjdira Anas M. Ali Wadii Boulila Zahra Moammeri Ahmad Mahmoudi-Aznaveh Ali Karbasi Hossein Motamednia Liangyan Li Guanhua Zhao Kevin Le Yimo Ning Haoxuan Huang Jun Chen
This is my paper · ORCID
classification cs.CV
keywords imagesuper-resolutionbicubicdownsamplingDIV2KbenchmarkPSNRrankingperceptualqualitymetricsTransformer-CNNhybridMambastate-spacemodelsone-stepdiffusionprior
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper reports the 2025 edition of a long-running annual image super-resolution competition, run in the classical setting where low-resolution inputs are produced by bicubic downsampling at a factor of 4. The central claim is that the benchmark has moved forward sharply: the best restoration entry reaches 33.46 dB PSNR on the 100-image hidden test set, more than 1.5 dB above the previous year's winning score, and ten entries exceed 31 dB. A newly added perceptual track, ranked by a composite score over six quality metrics, is won by an entry scoring 4.3472. The report attributes these gains to specific practices: hybrid Transformer/convolution designs, Mamba state-space models, progressive patch-size training, frequency-domain losses, and diffusion priors combined with CLIP-based text-prompt losses. If the reported scores are reproduced by the submitted code, these results define the new state of the art for classical super-resolution.

What carries the argument

The machinery that carries the results has two parts. Evaluation: restoration rankings rest on PSNR computed on the Y channel after cropping a 4-pixel border, while perceptual rankings rest on the composite score $\mathrm{Score} = (1-\mathrm{LPIPS}) + (1-\mathrm{DISTS}) + \mathrm{CLIPIQA} + (\mathrm{MANIQA}+\mathrm{MUSIQ})/100 + \max(0,(10-\mathrm{NIQE})/10)$, with hidden test high-resolution images and organizer code validation intended to keep the comparison fair. Method: for the restoration winner, the load-bearing mechanism is the dynamic fusion of a transformer branch (HAT) and a convolutional branch (NAFNet) trained with alternating L1/L2/wavelet losses and progressive patch sizes; for the perceptual winner, it is a one-step diffusion model acting as a fixed generative prior on top of a Mamba upsampler, steered by an L1 plus LPIPS plus CLIP text-prompt loss toward a "Good photo" embedding and away from a "Bad photo" embedding.

What would settle it

Re-running the submitted code and released models on the public validation split and comparing PSNR and perceptual scores to the recorded numbers would settle reproducibility; inspecting the top teams' training data manifests and logs for the hidden test high-resolution images would settle whether the no-test-data rule held.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the classical ×4 super-resolution benchmark is not saturated. The top restoration solution dynamically fuses a transformer attention network (HAT) with a convolutional network (NAFNet), trains alternately with L1, L2, and stationary-wavelet-transform losses over progressively larger patches, and reaches 33.46 dB PSNR, beating last year's best of 31.94 dB. The top perceptual solution instead uses a fine-tuned Mamba-based upsampler followed by a frozen one-step diffusion model, trained with a perceptual loss and a CLIP text-prompt loss, and reaches 4.3472 on the composite perceptual score. The paper reads these results as evidence that hybrid architectures and generative priors now set the standard, and that the field's two objectives—pixel fidelity and visual realism—require different designs.

Load-bearing premise

The reported rankings depend on the integrity of the competition: if any top team trained on the hidden high-resolution test images, or if submitted code does not reproduce the submitted scores, the state-of-the-art claims collapse, and the paper reports only an organizers' code check, not an independent audit.

Editorial extensions

If this is right

  • The classical ×4 benchmark still has measurable headroom: a jump of more than 1.5 dB over the previous year's winner means that hybrid architectures and larger training corpora translate directly into fidelity gains.
  • The new composite perceptual score gives future super-resolution work a single reproducible target that mixes reference-based and no-reference metrics, allowing optimization without a human study at every step.
  • The leading solutions share transferable training techniques—progressive patch enlargement, wavelet-domain loss, dynamic model fusion—that are not tied to one architecture.
  • With ten teams above 31 dB, the previous year's state-of-the-art score is now a routine baseline, shifting the practical question from whether a method can reach it to how efficiently it can exceed it.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the PSNR gap between the restoration winner (33.46 dB) and the perceptual winner (22.53 dB) suggests the field has split into two near-incompatible optima; combining fidelity and texture realism in one model would require a different reward than either track currently uses.
  • Editorial inference: because the composite perceptual score includes a no-reference NIQE term, future submissions may be able to inflate their rank by targeting that term; checking the score against human preference judgments would tell whether the ranking measures what it claims.
  • Editorial inference: the winning restoration entry's custom two-million-image training set makes the architecture contribution hard to isolate; an ablation that fixes the architecture and varies only the data scale would quantify how much of the 1.5 dB gain is data versus design.
  • Editorial inference: the same dynamic fusion of global-attention and local-convolution branches, plus progressive patch training, is a plausible recipe for other ill-posed restoration tasks such as denoising and face restoration, where both long-range context and fine detail matter.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper reports the organization and outcomes of the NTIRE 2025 image super-resolution (x4) challenge. The challenge follows the classical bicubic degradation setting and contains two tracks: a restoration track ranked by PSNR on the hidden DIV2K test set, and a perceptual track ranked by a composite score of seven IQA metrics defined in Eq. (1). The paper describes the datasets, evaluation protocol (4-pixel border exclusion, Y channel, Codalab submissions with code validation), fairness rules, and the results table (Table 1). It then details the top five methods (SamsungAICamera, SNUCV, BBox, MicroSR, XiaomiMM) and states that the remaining team descriptions are in the supplementary material. The central empirical claims are that SamsungAICamera achieves the top restoration PSNR of 33.46 dB, that SNUCV has the highest perceptual score of 4.3472, and that two teams surpass last year's best PSNR of 31.94 dB.

Significance. If the reported results are trustworthy, the challenge establishes a new state of the art for classical bicubic x4 SR on DIV2K, with a substantial improvement over the NTIRE 2024 winner. The paper has clear strengths: the evaluation protocol is explicit (Sec. 2.2), the test HR images were hidden, the evaluation script is publicly available, and the main results table is internally consistent. As a community benchmark report, it also documents a range of practical techniques (hybrid Transformer-CNN designs, Mamba integration, multi-stage training, CLIP-based filtering, diffusion-based perceptual enhancement) that are useful to practitioners. The main caveats are the absence of quantified code-validation tolerance, the arbitrary perceptual composite score, and the missing supplementary material that is repeatedly cited as containing most of the method descriptions.

major comments (3)
  1. [Sec. 2.2 and Sec. 3.1] The paper states that 'Code submitted by participants is used for reproduction and verification, with small discrepancies in precision being considered acceptable,' but it does not report the accepted tolerance, the per-team reproduction results, or any independent audit. This is load-bearing because the headline claim that 'two teams surpass last year's best PSNR score (31.94 dB)' rests on BBox at 31.97 dB, a margin of only 0.03 dB that lies within the unquantified 'small discrepancies' the protocol permits. As presented, the state-of-the-art improvement claim is unverified; please provide a table of reproduced scores, the exact acceptable deviation, and an analysis of whether the rankings are stable under that tolerance.
  2. [Eq. (1), Sec. 2.2 and Sec. 3] The perceptual score is an ad hoc sum of seven metrics, with arbitrary scaling choices such as MUSIQ/100 and max(0,(10-NIQE)/10), and no validation against human opinion scores or sensitivity analysis. Since the Track 2 ranking, including the claim that SNUCV 'ranks first with the highest perceptual score (4.3472),' is determined entirely by this formula, the perceptual ranking is only as meaningful as the unstated rationale for these weights and scales. Please justify the formula or demonstrate that the top rankings are robust to reasonable variations of the weights.
  3. [Sec. 5 and Abstract] Section 5 and the challenge-results discussion state that the remaining teams' methods and implementation details are in Sec. A of the supplementary materials, but no supplementary material is included in this submission. The abstract promises summaries of the methods of each team, yet only five teams are described in the main text. This gap prevents the paper from delivering its stated coverage; please include the supplementary or move essential method descriptions into the main text.
minor comments (4)
  1. [Sec. 2.1] The sentence 'To ensure fairness, participants do not have access to the high-resolution (HR) images from the DIV2K validation set except during the testing phase' is confusing because the testing-phase HR images are said to remain hidden; clarify whether validation HR are available for validation and only test HR are hidden.
  2. [Table 1 and Sec. 3.2] The paper says that 25 teams submitted valid entries, but Table 1 lists 26 rows, including Aimanga and IPCVTeam with N/A ranks; the comparison 'from 20 to 24' against last year also does not match the table; please reconcile the participant counts.
  3. [Sec. 4.1] There are typographical issues such as 'Trainning strategy' and the duplication of the caption 'Team SamsungAICamera' for both Fig. 1 and Fig. 2; figure captions should be distinct and descriptive.
  4. [Eq. (1)] Metric names are inconsistently capitalized (ManIQA vs. MANIQA), and the ranges needed to interpret terms such as MUSIQ/100 and max(0,(10-NIQE)/10) are not stated; a sentence explaining the metric ranges would help reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: challenge results are externally measured benchmark outcomes, not derived from fitted inputs.

full rationale

This paper is an empirical competition report, not a derivation. The rankings in Sec. 3 and Table 1 are direct measurements of submitted SR outputs against the hidden DIV2K test set, evaluated with standard metrics. Eq. 1 defines the perceptual score used to rank Track 2; it is an evaluation rule, not a fitted model, so the ranking is the score by design rather than a circular derivation. The organizers' self-citations (e.g., HAT [3], DAT [4], NTIRE 2024 [5]) provide architectural baselines and the previous year's measured best PSNR; those are externally falsifiable benchmark results, not assumptions that smuggle in this year's conclusions. The only substantive caveat is verification: the code-validation step in Sec. 2.2 is described as accepting 'small discrepancies in precision' without quantified tolerance, and no independent audit of the no-DIV2K-test-HR rule is reported. That is an integrity/verifiability concern about the benchmark, not a circularity in the paper's reasoning.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central benchmark claims rest primarily on the evaluation protocol and the integrity of the competition. I count no fitted parameters in the paper's own analysis aside from the protocol choices for the perceptual score and border exclusion; there are no invented entities. The main assumptions are the classical bicubic degradation setting, the validity of the chosen metrics, and the fairness of the submission process.

free parameters (2)
  • Perceptual score weights and NIQE threshold = Equal unit weights after rescaling; NIQE threshold 10
    Eq. 1 combines seven metrics with hand-picked weights and a threshold of 10 for NIQE. The Track 2 ranking is determined by this formula, but the weights are not fitted or justified by any user study.
  • Evaluation border exclusion = 4 pixels
    Sec. 2.2 specifies that a 4-pixel border is excluded before metric computation; this hand-set protocol choice can slightly affect PSNR/SSIM values.
assumptions (3)
  • domain assumption Bicubic downsampling at x4 is the target degradation model
    Sec. 2.1 constructs LR images via bicubic interpolation; this classical setting is adopted from prior work [50, 69].
  • domain assumption The chosen metrics (PSNR and seven perceptual metrics) reflect the desired qualities
    Sec. 2.2 ranks the restoration track on PSNR and the perceptual track on a composite of LPIPS, DISTS, CLIP-IQA, MANIQA, MUSIQ, NIQE; no independent validation against human opinion is presented.
  • domain assumption Participants followed the competition rules and their code reproduces the submitted results
    Sec. 3.3 prohibits use of DIV2K test HR images; Sec. 2.2 says organizers validate code, but no independent audit is described.

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Cite this review

Pith. "Pith review of NTIRE 2025 Challenge on Image Super-Resolution (x4): Methods and Results." pith.science (2026). https://pith.science/paper/7ZWOS33B

@misc{pith2026250414582,
  author       = {Pith},
  title        = {Pith review of: NTIRE 2025 Challenge on Image Super-Resolution (x4): Methods and Results},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7ZWOS33B}},
  note         = {Machine review of arXiv:2504.14582}
}
abstract

This paper presents the NTIRE 2025 image super-resolution ($\times$4) challenge, one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025. The challenge aims to recover high-resolution (HR) images from low-resolution (LR) counterparts generated through bicubic downsampling with a $\times$4 scaling factor. The objective is to develop effective network designs or solutions that achieve state-of-the-art SR performance. To reflect the dual objectives of image SR research, the challenge includes two sub-tracks: (1) a restoration track, emphasizes pixel-wise accuracy and ranks submissions based on PSNR; (2) a perceptual track, focuses on visual realism and ranks results by a perceptual score. A total of 286 participants registered for the competition, with 25 teams submitting valid entries. This report summarizes the challenge design, datasets, evaluation protocol, the main results, and methods of each team. The challenge serves as a benchmark to advance the state of the art and foster progress in image SR.

Figures

Figures reproduced from arXiv: 2504.14582 by the authors.

Figure 2
Figure 2. Team SamsungAICamera low-resolution versions. While significant advances have been made in recent years, existing methods often face chal￾lenges in effectively capturing both global context and fine local details simultaneously. To address these limitations, SamsungAICamera’s ap￾proach combines two powerful networks: the transformer￾based network HAT [3] and the convolution-based network NAFnet [2]. HAT try to lever… view at source ↗
Figure 1
Figure 1. Team SamsungAICamera 3.4. Conclusions The insights gained from analyzing the results of the NTIRE 2025 image super-resolution (SR) challenge are summa￾rized as follows: 1. The integration of Transformer and CNN hybrid archi￾tectures has demonstrated exceptional performance in modeling global context and reconstructing local details, achieving a balanced approach. 2. Mamba-based state space models have been widely ad… view at source ↗
Figure 3
Figure 3. Team SNUCV increases, the performance can gradually improve. As for training loss, they train the model by alternately iterating L1 loss, L2 loss, and Stationary Wavelet Transform(SWT) loss [26]. They found that adding SWT loss during training helps the model escape from local optima. Their model was trained on an A100 80G GPU. 4.2. SNUCV Description. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Team BBox. Swin Transformer Conv Concat Input Ouput S-Layer S-Layer S-Layer S-Layer Conv Reconstruction Dense Residual Connection [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Team MicroSR Implementation Details. The training dataset comprises DIV2K, LSDIR, Flickr2K, and selected Unsplash [1] datasets. To augment the training data, the team members implement random flip and rotation strategies. For the HAT model, the team members initialize …
Figure 6
Figure 6. Figure 6: Team XiaomiMM To further enhance performance, the team integrates a neural degradation algorithm [42] for data augmentation. The training data includes the DIV2K dataset [50], sup￾plemented with real-world paired images [19], resulting in a more diverse dataset and imp…

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.