Self-similarity prior
WebJan 31, 2024 · Our approach differed from typical NSS based denoising methods, which only exploited the nonlocal self-similarity (NSS) prior of noisy image or natural images, two NSS priors (i.e., NSS priors of noisy image and natural images) were used for image denoising. To boost the performance of image denoising, we proposed the concept of group sparsity ... WebAbstract: The nonlocal self-similarity in natural image provides an effective prior for single image super-resolution (SISR), which is beneficial to contextual information capture and …
Self-similarity prior
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WebApr 12, 2024 · Revisiting Self-Similarity: Structural Embedding for Image Retrieval Seongwon Lee · Suhyeon Lee · Hongje Seong · Euntai Kim LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data ... PEAL: Prior-embedded Explicit Attention Learning for low-overlap Point Cloud Registration WebJun 11, 2024 · Many patterns in nature exhibit self-similarity: they can be compactly described via self-referential transformations. Said patterns commonly appear in natural and artificial objects, such as molecules, shorelines, galaxies and even images.
WebAbstract: The image denoising model based on non-local self-similarity prior (NSS) has received extensive attention in recent years because of the repeated structure of natural image patches. Similar patches collected by exploiting NSS prior are sparse, which can be used to estimate potential lowrank subspace. WebAug 21, 2024 · In this paper, we propose a novel approach for image restoration by simultaneously considering internal and external nonlocal self-similarity (SNSS) priors …
WebOct 30, 2024 · Nonlocal self-similarity (NSS), which is also a popular prior model addressed in the nonlocal means (NLM) algorithm, can enhance the function of detail preservation [10]. WebFeb 18, 2016 · In particular, the use of image nonlocal self-similarity (NSS) prior, which refers to the fact that a local patch often has many nonlocal similar patches to it across …
WebApr 8, 2024 · “Self-similarity” is a common characteristic of medical images. That is, small-scale features often appear in multiple locations in the image frequently. Therefore, the …
WebIn our inversion strategy, the external optimization knowledge, and the intrinsic local sparsity and nonlocal self-similarity prior of model are used jointly for FWI. Computational results demonstrate the proposed method is obviously superior to existing inversion methods both qualitatively and quantitatively, including total variation FWI ... like good brownies crossword clueWebMoreover, the anomaly prior is developed by combining the nonlocal self-similarity and Markov random field priors for a Boolean label map which takes the spatial correlations of the image into consideration. Compared with some classical anomaly detection methods, the experiments on datasets show that the proposed method can effectively improve ... like gooey butter cake crosswordWebOct 27, 2024 · For the internal spatio-temporal feature self-similarity, we improve the accuracy and robustness of similarity matching by proposing a similarity measure strategy based on spatio-temporal moment feature similarity and structural similarity. The external nonlocal similarity prior constraint is learned by the patch group-based Gaussian mixture … hotels getaway near san franciscoWebOct 4, 2024 · First, the authors construct a statistical prior that requires maximising the similarity between the low- and high-resolution image pairs. Then, the authors develop an … like gods of the sunlike good penmanship crosswordWebestablished by combining the GMM with self-similarity prior maximisation, to efficiently solve the proposed SR optimisation model. • Demonstrate the superior performance of the proposed model and analyse the difference with state-of-the-art models for subjective and objective evaluations. Fig. 1 Self-similarity property in natural images. like gory films crosswordWebAug 21, 2024 · In 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-of-the-art techniques in image processing, such as denoising, super-resolution, … hotels gilroy california