Journal ArticleDOI
Adaptive Norm Selection for Regularized Image Restoration and Super-Resolution
TLDR
A method to adaptively determine the optimal norms for both fidelity term and regularization term in the (SR) restoration model is proposed, Inspired by a generalized likelihood ratio test, to solve the norm of the fidelity term.Abstract:
In the commonly employed regularization models of image restoration and super-resolution (SR), the norm determination is often challenging. This paper proposes a method to adaptively determine the optimal norms for both fidelity term and regularization term in the (SR) restoration model. Inspired by a generalized likelihood ratio test, a piecewise function is proposed to solve the norm of the fidelity term. This function can find the stable norm value in a certain number of iterations, regardless of whether the noise type is Gaussian, impulse, or mixed. For the regularization norm, the main advantage of the proposed method is that it is locally adaptive. Specifically, it assigns different norms for different pixel locations, according to the local activity measured by a structure tensor metric. The proposed method was tested using different types of images. The experimental results and error analyses verify the efficacy of the method.read more
Citations
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Journal ArticleDOI
Image super-resolution
TL;DR: This paper aims to provide a review of SR from the perspective of techniques and applications, and especially the main contributions in recent years, and discusses the current obstacles for future research.
Journal ArticleDOI
An Integrated Framework for the Spatio–Temporal–Spectral Fusion of Remote Sensing Images
TL;DR: The proposed integrated fusion framework can achieve the integrated fusion of multisource observations to obtain high spatio-temporal-spectral resolution images, without limitations on the number of remote sensing sensors.
Journal ArticleDOI
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.
TL;DR: Wang et al. as mentioned in this paper proposed a feature learning framework for hyperspectral images spectral-spatial feature representation and classification, which learns a latent low dimensional subspace by projecting the spectral and spatial feature into a common feature space, where the complementary information has been effectively exploited.
Journal ArticleDOI
PLTD: Patch-Based Low-Rank Tensor Decomposition for Hyperspectral Images
TL;DR: This paper proposes a novel HSI compression and reconstruction algorithm via patch-based low-rank tensor decomposition (PLTD), which simultaneously removes the redundancy in both the spatial and spectral domains in a unified framework.
Journal ArticleDOI
A Spatial and Temporal Nonlocal Filter-Based Data Fusion Method
TL;DR: In this paper, the spatial and temporal nonlocal filter-based fusion model (STNLFFM) is proposed to enhance the prediction capacity and accuracy, especially for complex changed landscapes.
References
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Journal ArticleDOI
Super-resolution image reconstruction: a technical overview
TL;DR: The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts to present the technical review of various existing SR methodologies which are often employed.
Journal ArticleDOI
Fast and robust multiframe super resolution
TL;DR: This paper proposes an alternate approach using L/sub 1/ norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models and demonstrates its superiority to other super-resolution methods.
Journal ArticleDOI
Limits on super-resolution and how to break them
Simon Baker,Takeo Kanade +1 more
TL;DR: This work derives a sequence of analytical results which show that the reconstruction constraints provide less and less useful information as the magnification factor increases, and proposes a super-resolution algorithm which attempts to recognize local features in the low-resolution images and then enhances their resolution in an appropriate manner.
Proceedings ArticleDOI
Limits on super-resolution and how to break them
Simon Baker,Takeo Kanade +1 more
TL;DR: An algorithm is proposed that learns recognition-based priors for specific classes of scenes, the use of which gives far better super-resolution results for both faces and text.
Journal ArticleDOI
Iterative methods for total variation denoising
Curtis R. Vogel,M. E. Oman +1 more
TL;DR: A fixed point algorithm for minimizing a TV penalized least squares functional is presented and compared with existing minimization schemes, and a variant of the cell-centered finite difference multigrid method of Ewing and Shen is implemented for solving the (large, sparse) linear subproblems.