Papers › Blind Super-Resolution With Iterative Kernel Correction

Blind Super-Resolution With Iterative Kernel Correction

6 Apr 2019CVPR 2019 6arXiv:1904.03377archive 2025-07-28

Jinjin Gu, Hannan Lu, WangMeng Zuo, Chao Dong

Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. Most of these methods assume that the blur kernel during downsampling is predefined/known (e.g., bicubic). However, the blur kernels involved in real applications are complicated and unknown, resulting in severe performance drop for the advanced SR methods. In this paper, we propose an Iterative Kernel Correction (IKC) method for blur kernel estimation in blind SR problem, where the blur kernels are unknown. We draw the observation that kernel mismatch could bring regular artifacts (either over-sharpening or over-smoothing), which can be applied to correct inaccurate blur kernels. Thus we introduce an iterative correction scheme -- IKC that achieves better results than direct kernel estimation. We further propose an effective SR network architecture using spatial feature transform (SFT) layers to handle multiple blur kernels, named SFTMD. Extensive experiments on synthetic and real-world images show that the proposed IKC method with SFTMD can provide visually favorable SR results and the state-of-the-art performance in blind SR problem.

PaperPDFConference PDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="1904.03377")

Code

Syntology Ran 2 of 6 code samples harvested from 2 repositories linked to this paper; 4 have no recorded run. Of those that ran: 2 ran with no contract checked.

By repository: community (archive-listed): 6 samples from 2 repositories, 2 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

sweetcocoa/IKC mentioned on GitHubpytorch report
yuanjunchai/IKC mentioned on GitHubpytorchApache-2.0 report
Lornatang/SFTMD-PyTorch pytorchApache-2.0 report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

6 samples harvested; 2 ran; 0 honoured the contract we drafted; 4 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

2ran
4unverified

Licence: 0 of the 6 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

calculate_psnr yuanjunchai/IKC/metrics/calculate_PSNR_SSIM.py community (archive-listed) ran fingerprinted Apache-2.0 (permissive) · 9394ff2c25b8a988 · report
ssim yuanjunchai/IKC/metrics/calculate_PSNR_SSIM.py community (archive-listed) ran fingerprinted Apache-2.0 (permissive) · ba721aeacc165234 · report
calculate_ssim yuanjunchai/IKC/metrics/calculate_PSNR_SSIM.py community (archive-listed) unverified Apache-2.0 (permissive) · 5aef99e58703c44a · report
image2tensor Lornatang/SFTMD-PyTorch/imgproc.py community (archive-listed) unverified Apache-2.0 (permissive) · 1df4cd01e272e4cc · report
principal_component_analysis Lornatang/SFTMD-PyTorch/imgproc.py community (archive-listed) unverified Apache-2.0 (permissive) · b757e6fc17cab8f0 · report
tensor2image Lornatang/SFTMD-PyTorch/imgproc.py community (archive-listed) unverified Apache-2.0 (permissive) · bc562954fab9c9fb · report

Tasks

Blind Super-ResolutionImage Super-ResolutionSuper-Resolution

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Blind Super-Resolution BSD100 - 2x upscaling IKC PSNR 31.36 #3 of 3 Archive leaderboard report
Blind Super-Resolution BSD100 - 2x upscaling IKC SSIM 0.8790 #3 of 3 Archive leaderboard report
Blind Super-Resolution BSD100 - 3x upscaling IKC PSNR 28.56 #2 of 2 Archive leaderboard report
Blind Super-Resolution BSD100 - 3x upscaling IKC SSIM 0.8493 #2 of 2 Archive leaderboard report
Blind Super-Resolution BSD100 - 4x upscaling IKC PSNR 27.29 #3 of 3 Archive leaderboard report
Blind Super-Resolution BSD100 - 4x upscaling IKC SSIM 0.8014 #3 of 3 Archive leaderboard report
Blind Super-Resolution Manga109 - 2x upscaling IKC PSNR 36.06 #3 of 3 Archive leaderboard report
Blind Super-Resolution Manga109 - 2x upscaling IKC SSIM 0.9474 #3 of 3 Archive leaderboard report
Blind Super-Resolution Manga109 - 3x upscaling IKC PSNR 28.21 #2 of 2 Archive leaderboard report
Blind Super-Resolution Manga109 - 3x upscaling IKC SSIM 0.8739 #2 of 2 Archive leaderboard report
Blind Super-Resolution Manga109 - 4x upscaling IKC PSNR 29.9 #3 of 3 Archive leaderboard report
Blind Super-Resolution Manga109 - 4x upscaling IKC SSIM 0.8793 #3 of 3 Archive leaderboard report
Blind Super-Resolution Set14 - 2x upscaling IKC PSNR 32.82 #3 of 3 Archive leaderboard report
Blind Super-Resolution Set14 - 2x upscaling IKC SSIM 0.8999 #3 of 3 Archive leaderboard report
Blind Super-Resolution Set14 - 3x upscaling IKC PSNR 29.46 #2 of 2 Archive leaderboard report
Blind Super-Resolution Set14 - 3x upscaling IKC SSIM 0.8229 #2 of 2 Archive leaderboard report
Blind Super-Resolution Set14 - 4x upscaling IKC PSNR 28.26 #3 of 3 Archive leaderboard report
Blind Super-Resolution Set14 - 4x upscaling IKC SSIM 0.7688 #3 of 3 Archive leaderboard report
Blind Super-Resolution Set5 - 2x upscaling IKC PSNR 36.62 #3 of 3 Archive leaderboard report
Blind Super-Resolution Set5 - 2x upscaling IKC SSIM 0.9658 #3 of 3 Archive leaderboard report
Blind Super-Resolution Set5 - 3x upscaling IKC PSNR 32.16 #2 of 2 Archive leaderboard report
Blind Super-Resolution Set5 - 3x upscaling IKC SSIM 0.942 #2 of 2 Archive leaderboard report
Blind Super-Resolution Set5 - 4x upscaling IKC PSNR 31.52 #3 of 3 Archive leaderboard report
Blind Super-Resolution Set5 - 4x upscaling IKC SSIM 0.9278 #3 of 3 Archive leaderboard report
Blind Super-Resolution Urban100 - 2x upscaling IKC PSNR 30.36 #3 of 3 Archive leaderboard report
Blind Super-Resolution Urban100 - 2x upscaling IKC SSIM 0.8949 #3 of 3 Archive leaderboard report
Blind Super-Resolution Urban100 - 3x upscaling IKC PSNR 25.94 #2 of 2 Archive leaderboard report
Blind Super-Resolution Urban100 - 3x upscaling IKC SSIM 0.8165 #2 of 2 Archive leaderboard report
Blind Super-Resolution Urban100 - 4x upscaling IKC PSNR 25.33 #3 of 3 Archive leaderboard report
Blind Super-Resolution Urban100 - 4x upscaling IKC SSIM 0.776 #3 of 3 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Spatial Feature Transform

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections