Nonlocal Self-SimilaritySparse RepresentationIn the past decade, the sparsity prior of image is investigated and utilized widely as the development of compressed sensing theory. The dictionary learning combined with the convex optimization methods promotes the sparse representation to be one of the state...
Young-Joo Han and Ha-Jin Yu, "SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with Nonlocal Self-Similarity", IJCAI, 2023. PDF BibTex Arxiv Abstract Recently, numerous studies have been conducted on supervised learning-based image denoising methods. However, these methods rely...
According to the nonlocal self-similarity property of natural images, group-based simultaneous sparse coding (GSSC) model assumes that nonlocal similar patches have similar sparse representations in a given dictionary and have been widely used in various image inverse problems. Inspired by the success...
This prior information can be divided into three commonly used categories: low-rankness, local piecewise smoothness, and nonlocal self-similarity (NSS) priors. Although existing methods based on these priors have gained considerable attention, the majority of studies utilize only one or two of ...
A nonlocal feature self-similarity based tensor completion method for video recovery ? 2024 Elsevier B.V.The nuclear norm-based tensor completion method effectively recovers missing multidimensional data in videos by minimizing the truncate... S Lu,P Wang,Dai C.Liu C.Dai S.Zhu W.Zhang Y. -...
Color image demosaicking using inter-channel correlation and nonlocal self-similarity •We formulate the demosaicking problem in a perspective of image reconstruction.•We model color images by considering different types of prior inform... C Kan,PLK Ding,B Li - 《Signal Processing Image Communica...
The global correlation across spectrum (GCS) and nonlocal self-similarity (NSS) over space are two important characteristics for HSI. In this paper, a nonlocal low-rank regularized CANDECOMP/PARAFAC (CP) tensor decomposition (NLR-CPTD) is proposed to fully utilize these two intrins...
Moreover, to improve the accuracy, these methods consider other priors, such as nonlocal self-similarity, sparsity, and piecewise smoothness [30], [31], [32], [33]. Specifically, Li et al. [21] applied the Tucker decomposition for the HR-HSI, then imposed the ℓ1-norm on the core...
Hyperspectral image (HSI) possesses three intrinsic characteristics: the global correlation across spectral domain, t he nonlocal self-similarity across spatial domain, and the local smooth structure across both spatial and spectral domains. This paper proposes a novel tensor based approach to handle ...
Young-Joo Han and Ha-Jin Yu, "SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with Nonlocal Self-Similarity", IJCAI, 2023.PDFBibTexArxiv Abstract Recently, numerous studies have been conducted on supervised learning-based image denoising methods. However, these methods rely on...