Journal ArticleDOI
Noise adaptive super-resolution from single image via non-local mean and sparse representation
TLDR
A robust super-resolution algorithm which adapts itself based on the noise-level in the image, which demonstrates better efficacy for optical and range images under different types and strengths of noise.About:
This article is published in Signal Processing.The article was published on 2017-03-01. It has received 34 citations till now. The article focuses on the topics: Gradient noise & Gaussian noise.read more
Citations
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Journal ArticleDOI
Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects
Shuihua Wang,M. Emre Celebi,Yudong Zhang,Xiang Yu,Siyuan Lu,Xujing Yao,Qinghua Zhou,Martínez-García Miguel,Yingli Tian,Juan Manuel Górriz,Ivan Tyukin +10 more
TL;DR: An overview of the advances in data preprocessing in biomedical data fusion is provided in this article, along with insights stemming from new developments in the field, and an overview of new techniques for data fusion methods are discussed.
Journal ArticleDOI
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks
TL;DR: In this paper, an end-to-end trainable deep convolutional neural network is designed to perform super-resolution on compressed images, which jointly reduces compression artifacts and improves image resolution.
Journal ArticleDOI
Noise-robust image fusion with low-rank sparse decomposition guided by external patch prior
TL;DR: A novel discriminative dictionary learning algorithm is developed to construct two dictionaries so as to decompose the input image into LR-and-S components and enforce spatial morphology constraint on each dictionary.
Journal ArticleDOI
Adaptive iterative reconstruction based on relative total variation for low-intensity computed tomography
Changcheng Gong,Li Zeng +1 more
TL;DR: Experiments on digital phantoms and real CT projections verified superiorities of POCS-ARTV, and the ability of the L-curve method to determine an acceptable regularization parameter, which can effectively suppress noise and preserve image structures.
Journal ArticleDOI
Depth Map Restoration From Undersampled Data
TL;DR: This paper proposes a new approach to address issues in a unified framework of depth map restoration, based on sparse representation, and suggests an alternative method of reconstructing dense depth map from very sparse non- uniformly sampled depth data by sequential cascading of uniform and non-uniform upsampling techniques.
References
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Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Journal ArticleDOI
De-noising by soft-thresholding
TL;DR: The authors prove two results about this type of estimator that are unprecedented in several ways: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures.
Journal ArticleDOI
Ideal spatial adaptation by wavelet shrinkage
TL;DR: In this article, the authors developed a spatially adaptive method, RiskShrink, which works by shrinkage of empirical wavelet coefficients, and achieved a performance within a factor log 2 n of the ideal performance of piecewise polynomial and variable-knot spline methods.
Journal ArticleDOI
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
TL;DR: An algorithm based on an enhanced sparse representation in transform domain based on a specially developed collaborative Wiener filtering achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.
Proceedings ArticleDOI
A non-local algorithm for image denoising
TL;DR: A new measure, the method noise, is proposed, to evaluate and compare the performance of digital image denoising methods, and a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image is proposed.