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BM3D Frames and Variational Image Deblurring

About

A family of the Block Matching 3-D (BM3D) algorithms for various imaging problems has been recently proposed within the framework of nonlocal patch-wise image modeling [1], [2]. In this paper we construct analysis and synthesis frames, formalizing the BM3D image modeling and use these frames to develop novel iterative deblurring algorithms. We consider two different formulations of the deblurring problem: one given by minimization of the single objective function and another based on the Nash equilibrium balance of two objective functions. The latter results in an algorithm where the denoising and deblurring operations are decoupled. The convergence of the developed algorithms is proved. Simulation experiments show that the decoupled algorithm derived from the Nash equilibrium formulation demonstrates the best numerical and visual results and shows superiority with respect to the state of the art in the field, confirming a valuable potential of BM3D-frames as an advanced image modeling tool.

Aram Danielyan, Vladimir Katkovnik, Karen Egiazarian• 2011

Related benchmarks

TaskDatasetResultRank
3DGS Compression Artifact RestorationDeepBlending (novel views)
LPIPS0.209
25
3DGS Compression Artifact RestorationTanks & Temples (novel views)
LPIPS0.225
25
3DGS Compression Artifact RestorationMip-NeRF360 (novel views)
LPIPS0.308
25
PET Image DenoisingUDPET 1/4 dose level (External)
PSNR (dB)44.347
9
PET Image DenoisingUDPET 1/20 dose level (Internal)
PSNR (dB)41.068
9
PET Image DenoisingUDPET 1/50 dose level (External)
PSNR (dB)36.358
9
Image DeblurringStandard Images Gaussian Blur std 1.6 (test)
PSNR (Cameraman)27.08
8
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