To address this problem, we propose a new method to insert the Group-based sparse representation (GSR) into the convergence-related metric of FPM as the regularization term in this paper. We have carried out the experiments over both synthetic and real captured images, and the results ...
Unsupervised Band Selection Based on Group-Based Sparse RepresentationBand selection (BS) is one of the important topics in hyperspectral image data analysis. How to search the representative bands that can effectively represent the image with lower inter-band......
Group-based sparse representationNonlocal total variationJoint regularizationSplit Bregman iterationCompressive sensing (CS) has recently drawn considerable attentions in signal and image processing communities as a joint sampling and compression approach. Generally, the image CS reconstruction can be formulated...
called group-based sparse representation (GSR). The proposed GSR is able to sparsely represent natural images in the domain of group, which enforces the intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. Moreover, an effective self-adaptive diction...
The invention discloses a single-frame image super-resolution rebuilding method based on group sparse representation. The method comprises the following steps of: 1, building a training sample library of high-resolution images; then, using an orthogonal matching pursuit method for solving a sparse ...
-sparse signals via block orthogonal matching pursuit (BOMP) algorithm. Under some constraints on the minimum magnitude of the nonzero elements of the block ??-sparse signals, we prove that the support of the block ??-sparse signals can be exactly recovered by t...
Factor group-sparse regularization for efficient low-rank matrix recovery. Adv. Neural Inf. Process. Syst. 2019, 32, 5105–5115. [Google Scholar] Fang, L.; Zhuo, H.; Li, S. Super-resolution of hyperspectral image via superpixel-based sparse representation. Neurocomputing 2018, 273, 171–...
In this section, the proposed algorithm is introduced. It includes three main parts: temporal normalization and permutation, temporal similar patch search, and temporal group sparse representation. 2.1Temporal Normalization and Permutation Suppose that\( T \)multitemporal remote sensing images\( \left\...
The navigation error is not only the algebraic difference of vectors, but also strongly related to the vector's representation between different navigation frames (Tang et al., 2023) (Luo et al., 2021c). However, this is usually ignored in SOKF. Scholars have conducted a series of ...
Firstly, feature representation of the symmetric characteristics of face pattern is formulated as a structured sparsity problem and sparse group lasso is used to select the most effective local features for face detection. Secondly, minimal redundancy maximal relevance is used to remove the redundant ...