权值调整的依据(prior)是,一些图像(尤其是医学图像)往往具有“自相似性”,即小尺度特征倾向于在图片中的多个位置重复出现,因此全局搜索相似像素点有助于推断此处的像素值——这可以应用在图像超分与去噪之中。之所以不用卷积来做这件事,是因为卷积核只能关注局域(local)的信息,而如果要关注更大尺度(更远)的信息,...
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...
将该算法与块期望对数似然去噪算法(Expected Patch Log Likelihood,EPLL),加权核范数最小化算法(Weighted Nuclear Norm Minimization,WNNM),非局部自相似性块组学习算法(Patch Group Based Non-local Self-similarity Prior Learning for Image Denosing,... 翁丽源 - 《河北工业大学》 被引量: 0发表: 2020年 基于...
Various priors of natural image, such as gradient based prior, nonlocal self-similarity based prior etc., have been widely studied for noise removal. ... S Jia,L Ying,S Zhou,... 被引量: 1发表: 2015年 A novel diffusivity function-based image denoising for MRI medical images image denoisi...
对于超分辨率应用,non-local attention是非常流行的,因为它可以利用图像中的 self-similarity prior,因为一些小的 pattern 会在图像中重复出现。但是,直接应用 non-local 也会出现一些问题: the receptive field of features in deeper layers tend to be global, thus the mutual-correlation computation among deep ...
代码:https://github.com/HarukiYqM/Non-Local-Sparse-Attention 对于超分辨率应用,non-local attention是非常流行的,因为它可以利用图像中的 self-similarity prior,因为一些小的 pattern 会在图像中重复出现。但是,直接应用 non-local 也会出现一些问题:
1、单一的non-local block加在较浅层次效果显著。reasonable。高层次丢失的信息太多了，找不到细小的远...
Nonlocal self-similarity within natural images has become an increasingly popular prior in deep-learning models. Despite their successful image restoration performance, such models remain largely uninterpretable due to their black-box co... N Janjuvsevi'C,A Khalilian-Gourtani,A Flinker,... 被引...
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...
To address this problem, in this letter, we propose a novel blind deconvolution model that combines low-rank property, nonlocal similarity, and l0 sparsity prior. Low-rank property makes the proposed deblurring model robust to image noise. The joint utilization of nonlocal similarity and l0 ...