On the fly image denoising using patch ordering

Colak, Ozden; Ekşioğlu, Ender M.

doi:10.1016/j.eswa.2021.116192

Cited by 6 publications

(1 citation statement)

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“…A remedy is to use the image patch as the basic unit of modeling [6,7]. Thus, over the past few decades, the patch-based prior has been extensively studied and has achieved favorable image restoration performance, such as patch-based sparse representation [8][9][10][11][12][13] and patchbased image modeling [6,[14][15][16]. Recently, deep learning has also been adopted to learn image priors in a supervised manner and has spawned promising results in various image restoration applications [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Learning the Hybrid Nonlocal Self-Similarity Prior for Image Restoration

Yuan,

Liu,

Liang

et al. 2024

Mathematics

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As an immensely important characteristic of natural images, the nonlocal self-similarity (NSS) prior has demonstrated great promise in a variety of inverse problems. Unfortunately, most current methods utilize either the internal or the external NSS prior learned from the degraded image or training images. The former is inevitably disturbed by degradation, while the latter is not adapted to the image to be restored. To mitigate such problems, this work proposes to learn a hybrid NSS prior from both internal images and external training images and employs it in image restoration tasks. To achieve our aims, we first learn internal and external NSS priors from the measured image and high-quality image sets, respectively. Then, with the learned priors, an efficient method, involving only singular value decomposition (SVD) and a simple weighting method, is developed to learn the HNSS prior for patch groups. Subsequently, taking the learned HNSS prior as the dictionary, we formulate a structural sparse representation model with adaptive regularization parameters called HNSS-SSR for image restoration, and a general and efficient image restoration algorithm is developed via an alternating minimization strategy. The experimental results indicate that the proposed HNSS-SSR-based restoration method exceeds many existing competition algorithms in PSNR and SSIM values.

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