Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/CPAT+

CPAT+

Reported on 120 benchmarks across 4 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision60 results

Image Super-ResolutiononBSD100 - 2x upscaling
PSNR· 2024-07-23
32.66
best: 33.12 (WaveMixSR-V2)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononBSD100 - 2x upscaling
SSIM· 2024-07-23
0.9058
best: 0.9326 (WaveMixSR-V2)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononSet14 - 3x upscaling
PSNR· 2024-07-23
31.19
best: 31.55 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononSet14 - 3x upscaling
SSIM· 2024-07-23
0.8559
best: 0.8616 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononSet5 - 4x upscaling
PSNR· 2024-07-23
33.24
best: 33.38 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononSet5 - 4x upscaling
SSIM· 2024-07-23
0.9071
best: 0.9103 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononSet14 - 2x upscaling
PSNR· 2024-07-23
34.97
best: 35.36 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononSet14 - 2x upscaling
SSIM· 2024-07-23
0.928
best: 0.9311 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononSet14 - 4x upscaling
PSNR· 2024-07-23
29.36
best: 29.54 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononSet14 - 4x upscaling
SSIM· 2024-07-23
0.7996
best: 0.894 (Edge-informed SR)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononSet5 - 3x upscaling
PSNR· 2024-07-23
35.19
best: 35.35 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononSet5 - 3x upscaling
SSIM· 2024-07-23
0.9335
best: 0.938 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononManga109 - 4x upscaling
PSNR· 2024-07-23
32.85
best: 33.19 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononManga109 - 4x upscaling
SSIM· 2024-07-23
0.9318
best: 0.9366 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononUrban100 - 2x upscaling
PSNR· 2024-07-23
34.89
best: 35.24 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononUrban100 - 2x upscaling
SSIM· 2024-07-23
0.9487
best: 0.9516 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononManga109 - 3x upscaling
PSNR· 2024-07-23
35.77
best: 36.12 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononManga109 - 3x upscaling
SSIM· 2024-07-23
0.9563
best: 0.9588 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononSet5 - 2x upscaling
PSNR· 2024-07-23
38.72
best: 39.14 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononSet5 - 2x upscaling
SSIM· 2024-07-23
0.9635
best: 0.9663 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononManga109 - 2x upscaling
PSNR· 2024-07-23
40.59
best: 41.22 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononManga109 - 2x upscaling
SSIM· 2024-07-23
0.9816
best: 0.9846 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononUrban100 - 4x upscaling
PSNR· 2024-07-23
28.33
best: 28.72 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononUrban100 - 4x upscaling
SSIM· 2024-07-23
0.8425
best: 0.9481 (SPSR)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononUrban100 - 3x upscaling
PSNR· 2024-07-23
30.63
best: 31.07 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononUrban100 - 3x upscaling
SSIM· 2024-07-23
0.8934
best: 0.902 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononBSD100 - 4x upscaling
PSNR· 2024-07-23
28.06
best: 28.16 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononBSD100 - 4x upscaling
SSIM· 2024-07-23
0.7532
best: 0.851 (Edge-informed SR)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononBSD100 - 3x upscaling
PSNR· 2024-07-23
29.59
best: 29.67 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Image Super-ResolutiononBSD100 - 3x upscaling
SSIM· 2024-07-23
0.8177
best: 0.8256 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononBSD100 - 2x upscaling
PSNR· 2024-07-23
32.66
best: 33.12 (WaveMixSR-V2)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononBSD100 - 2x upscaling
SSIM· 2024-07-23
0.9058
best: 0.9326 (WaveMixSR-V2)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononSet14 - 3x upscaling
PSNR· 2024-07-23
31.19
best: 31.55 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononSet14 - 3x upscaling
SSIM· 2024-07-23
0.8559
best: 0.8616 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononSet5 - 4x upscaling
PSNR· 2024-07-23
33.24
best: 33.38 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononSet5 - 4x upscaling
SSIM· 2024-07-23
0.9071
best: 0.9103 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononSet14 - 2x upscaling
PSNR· 2024-07-23
34.97
best: 35.36 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononSet14 - 2x upscaling
SSIM· 2024-07-23
0.928
best: 0.9311 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononSet14 - 4x upscaling
PSNR· 2024-07-23
29.36
best: 29.54 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononSet14 - 4x upscaling
SSIM· 2024-07-23
0.7996
best: 0.894 (Edge-informed SR)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononSet5 - 3x upscaling
PSNR· 2024-07-23
35.19
best: 35.35 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononSet5 - 3x upscaling
SSIM· 2024-07-23
0.9335
best: 0.938 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononManga109 - 4x upscaling
PSNR· 2024-07-23
32.85
best: 33.19 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononManga109 - 4x upscaling
SSIM· 2024-07-23
0.9318
best: 0.9366 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononUrban100 - 2x upscaling
PSNR· 2024-07-23
34.89
best: 35.24 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononUrban100 - 2x upscaling
SSIM· 2024-07-23
0.9487
best: 0.9516 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononManga109 - 3x upscaling
PSNR· 2024-07-23
35.77
best: 36.12 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononManga109 - 3x upscaling
SSIM· 2024-07-23
0.9563
best: 0.9588 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononSet5 - 2x upscaling
PSNR· 2024-07-23
38.72
best: 39.14 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononSet5 - 2x upscaling
SSIM· 2024-07-23
0.9635
best: 0.9663 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononManga109 - 2x upscaling
PSNR· 2024-07-23
40.59
best: 41.22 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononManga109 - 2x upscaling
SSIM· 2024-07-23
0.9816
best: 0.9846 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononUrban100 - 4x upscaling
PSNR· 2024-07-23
28.33
best: 28.72 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononUrban100 - 4x upscaling
SSIM· 2024-07-23
0.8425
best: 0.9481 (SPSR)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononUrban100 - 3x upscaling
PSNR· 2024-07-23
30.63
best: 31.07 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononUrban100 - 3x upscaling
SSIM· 2024-07-23
0.8934
best: 0.902 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononBSD100 - 4x upscaling
PSNR· 2024-07-23
28.06
best: 28.16 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononBSD100 - 4x upscaling
SSIM· 2024-07-23
0.7532
best: 0.851 (Edge-informed SR)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononBSD100 - 3x upscaling
PSNR· 2024-07-23
29.59
best: 29.67 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
3D Object Super-ResolutiononBSD100 - 3x upscaling
SSIM· 2024-07-23
0.8177
best: 0.8256 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232

Graphs30 results

Super-ResolutiononBSD100 - 2x upscaling
PSNR· 2024-07-23
32.66
best: 33.12 (WaveMixSR-V2)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononBSD100 - 2x upscaling
SSIM· 2024-07-23
0.9058
best: 0.9326 (WaveMixSR-V2)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononSet14 - 3x upscaling
PSNR· 2024-07-23
31.19
best: 31.55 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononSet14 - 3x upscaling
SSIM· 2024-07-23
0.8559
best: 0.8616 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononSet5 - 4x upscaling
PSNR· 2024-07-23
33.24
best: 33.38 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononSet5 - 4x upscaling
SSIM· 2024-07-23
0.9071
best: 0.9103 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononSet14 - 2x upscaling
PSNR· 2024-07-23
34.97
best: 35.36 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononSet14 - 2x upscaling
SSIM· 2024-07-23
0.928
best: 0.9311 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononSet14 - 4x upscaling
PSNR· 2024-07-23
29.36
best: 29.54 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononSet14 - 4x upscaling
SSIM· 2024-07-23
0.7996
best: 0.894 (Edge-informed SR)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononSet5 - 3x upscaling
PSNR· 2024-07-23
35.19
best: 35.35 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononSet5 - 3x upscaling
SSIM· 2024-07-23
0.9335
best: 0.938 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononManga109 - 4x upscaling
PSNR· 2024-07-23
32.85
best: 33.19 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononManga109 - 4x upscaling
SSIM· 2024-07-23
0.9318
best: 0.9366 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononUrban100 - 2x upscaling
PSNR· 2024-07-23
34.89
best: 35.24 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononUrban100 - 2x upscaling
SSIM· 2024-07-23
0.9487
best: 0.9516 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononManga109 - 3x upscaling
PSNR· 2024-07-23
35.77
best: 36.12 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononManga109 - 3x upscaling
SSIM· 2024-07-23
0.9563
best: 0.9588 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononSet5 - 2x upscaling
PSNR· 2024-07-23
38.72
best: 39.14 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononSet5 - 2x upscaling
SSIM· 2024-07-23
0.9635
best: 0.9663 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononManga109 - 2x upscaling
PSNR· 2024-07-23
40.59
best: 41.22 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononManga109 - 2x upscaling
SSIM· 2024-07-23
0.9816
best: 0.9846 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononUrban100 - 4x upscaling
PSNR· 2024-07-23
28.33
best: 28.72 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononUrban100 - 4x upscaling
SSIM· 2024-07-23
0.8425
best: 0.9481 (SPSR)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononUrban100 - 3x upscaling
PSNR· 2024-07-23
30.63
best: 31.07 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononUrban100 - 3x upscaling
SSIM· 2024-07-23
0.8934
best: 0.902 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononBSD100 - 4x upscaling
PSNR· 2024-07-23
28.06
best: 28.16 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononBSD100 - 4x upscaling
SSIM· 2024-07-23
0.7532
best: 0.851 (Edge-informed SR)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononBSD100 - 3x upscaling
PSNR· 2024-07-23
29.59
best: 29.67 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
Super-ResolutiononBSD100 - 3x upscaling
SSIM· 2024-07-23
0.8177
best: 0.8256 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232

Methodology30 results

16konBSD100 - 2x upscaling
PSNR· 2024-07-23
32.66
best: 33.12 (WaveMixSR-V2)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konBSD100 - 2x upscaling
SSIM· 2024-07-23
0.9058
best: 0.9326 (WaveMixSR-V2)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konSet14 - 3x upscaling
PSNR· 2024-07-23
31.19
best: 31.55 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konSet14 - 3x upscaling
SSIM· 2024-07-23
0.8559
best: 0.8616 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konSet5 - 4x upscaling
PSNR· 2024-07-23
33.24
best: 33.38 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konSet5 - 4x upscaling
SSIM· 2024-07-23
0.9071
best: 0.9103 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konSet14 - 2x upscaling
PSNR· 2024-07-23
34.97
best: 35.36 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konSet14 - 2x upscaling
SSIM· 2024-07-23
0.928
best: 0.9311 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konSet14 - 4x upscaling
PSNR· 2024-07-23
29.36
best: 29.54 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konSet14 - 4x upscaling
SSIM· 2024-07-23
0.7996
best: 0.894 (Edge-informed SR)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konSet5 - 3x upscaling
PSNR· 2024-07-23
35.19
best: 35.35 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konSet5 - 3x upscaling
SSIM· 2024-07-23
0.9335
best: 0.938 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konManga109 - 4x upscaling
PSNR· 2024-07-23
32.85
best: 33.19 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konManga109 - 4x upscaling
SSIM· 2024-07-23
0.9318
best: 0.9366 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konUrban100 - 2x upscaling
PSNR· 2024-07-23
34.89
best: 35.24 (HMA†)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konUrban100 - 2x upscaling
SSIM· 2024-07-23
0.9487
best: 0.9516 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konManga109 - 3x upscaling
PSNR· 2024-07-23
35.77
best: 36.12 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konManga109 - 3x upscaling
SSIM· 2024-07-23
0.9563
best: 0.9588 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konSet5 - 2x upscaling
PSNR· 2024-07-23
38.72
best: 39.14 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konSet5 - 2x upscaling
SSIM· 2024-07-23
0.9635
best: 0.9663 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konManga109 - 2x upscaling
PSNR· 2024-07-23
40.59
best: 41.22 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konManga109 - 2x upscaling
SSIM· 2024-07-23
0.9816
best: 0.9846 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konUrban100 - 4x upscaling
PSNR· 2024-07-23
28.33
best: 28.72 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konUrban100 - 4x upscaling
SSIM· 2024-07-23
0.8425
best: 0.9481 (SPSR)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konUrban100 - 3x upscaling
PSNR· 2024-07-23
30.63
best: 31.07 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konUrban100 - 3x upscaling
SSIM· 2024-07-23
0.8934
best: 0.902 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konBSD100 - 4x upscaling
PSNR· 2024-07-23
28.06
best: 28.16 (DRCT-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konBSD100 - 4x upscaling
SSIM· 2024-07-23
0.7532
best: 0.851 (Edge-informed SR)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konBSD100 - 3x upscaling
PSNR· 2024-07-23
29.59
best: 29.67 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232
16konBSD100 - 3x upscaling
SSIM· 2024-07-23
0.8177
best: 0.8256 (Hi-IR-L)
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution arXiv:2407.16232