Image Denoising on FMD (test) benchmark leaderboard · SOTA2 Research

Image Denoising on FMD (test)

41.561PSNRQWD-GAN

Evaluation Results

Method	Links
QWD-GAN2025.09		41.561	—	—	0.978	0.986
N2V2025.09		40.986	—	—	0.956	0.942
RDDM2025.09		40.556	—	—	0.961	0.919
RIDNet2025.09		38.568	—	—	0.942	0.809
N2N2025.09		38.435	—	—	0.96	0.901
DnCNN2025.09		38.268	—	—	0.972	0.829
QWD-GAN2025.09		38.145	—	—	0.964	0.971
RDDM2025.09		38.059	—	—	0.953	0.915
N2N2025.09		37.596	—	—	0.948	0.886
N2V2025.09		37.359	—	—	0.941	0.923
QWD-GAN2025.09		37.191	—	—	0.956	0.969
RDDM2025.09		37.072	—	—	0.95	0.907
BM3D2025.09		36.924	—	—	0.929	1.134
QWD-GAN2025.09		36.749	—	—	0.945	0.962
CGanDeCompress2026.02		36.71	0.035	0.14	0.934	—
N2V2025.09		36.695	—	—	0.937	0.885
N2N2025.09		36.404	—	—	0.923	0.863
RDDM2025.09		36.223	—	—	0.949	0.903
DnCNN2025.09		36.212	—	—	0.946	0.815
N2N2025.09		36.21	—	—	0.937	0.874
RIDNet2025.09		36.014	—	—	0.926	0.795
N2V2025.09		35.281	—	—	0.933	0.856
DnCNN2025.09		34.636	—	—	0.937	0.801
RIDNet2025.09		34.561	—	—	0.917	0.767
BM3D2025.09		33.985	—	—	0.907	1.159
DnCNN2025.09		33.533	—	—	0.926	0.794
RDDM2025.09		33.452	—	—	0.938	0.893
RIDNet2025.09		33.343	—	—	0.905	0.754
QWD-GAN2025.09		33.311	—	—	0.936	0.951
N2N2025.09		32.405	—	—	0.919	0.849
N2V2025.09		32.156	—	—	0.92	0.821
BM3D2025.09		31.224	—	—	0.876	1.167
DnCNN2025.09		30.531	—	—	0.906	0.766
RIDNet2025.09		30.321	—	—	0.896	0.709
BM3D2025.09		29.564	—	—	0.883	1.176
Noisy2026.02		28.45	0.439	0.344	0.582	—
BM3D2025.09		24.423	—	—	0.808	1.198