Journal ArticleDOI
LLSURE: Local Linear SURE-Based Edge-Preserving Image Filtering
TLDR
A simple explicit image filter which can filter out noise while preserving edges and fine-scale details is derived, which has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks.Abstract:
In this paper, we propose a novel approach for performing high-quality edge-preserving image filtering. Based on a local linear model and using the principle of Stein's unbiased risk estimate as an estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the effectiveness of the new filter for various computer vision applications, including noise reduction, detail smoothing and enhancement, high dynamic range compression, and flash/no-flash denoising.read more
Citations
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Journal ArticleDOI
Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal
TL;DR: Zhang et al. as mentioned in this paper introduced a deep network architecture called DerainNet for removing rain streaks from an image, which directly learned the mapping relationship between rainy and clean image detail layers from data.
Journal ArticleDOI
Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering
TL;DR: Experimental results demonstrate that the proposed edge-preserving filtering based classification method can improve the classification accuracy significantly in a very short time and can be easily applied in real applications.
Journal ArticleDOI
Image denoising review: From classical to state-of-the-art approaches
TL;DR: This article focuses on classifying and comparing some of the significant works in the field of denoising and explains why some methods work optimally and others tend to create artefacts and remove fine structural details under general conditions.
Journal ArticleDOI
A survey of edge-preserving image denoising methods
Paras Jain,Vipin Tyagi +1 more
TL;DR: The main aim of this survey is to provide evolution of research in the direction of edge-preserving image denoising so as to make it easier for researchers to choose the method best suited to their aims.
Journal ArticleDOI
Graph Signal Denoising via Trilateral Filter on Graph Spectral Domain
TL;DR: This paper presents a graph signal denoising method with the trilateral filter defined in the graph spectral domain, and provides a parameter optimization technique to search for a regularization parameter that approximately minimizes the mean squared error of the data-dependent filter.
References
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Journal ArticleDOI
Nonlinear total variation based noise removal algorithms
TL;DR: In this article, a constrained optimization type of numerical algorithm for removing noise from images is presented, where the total variation of the image is minimized subject to constraints involving the statistics of the noise.
Journal ArticleDOI
Robust Real-Time Face Detection
Paul A. Viola,Michael Jones +1 more
TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Journal ArticleDOI
Scale-space and edge detection using anisotropic diffusion
Pietro Perona,Jitendra Malik +1 more
TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Proceedings ArticleDOI
Bilateral filtering for gray and color images
Carlo Tomasi,Roberto Manduchi +1 more
TL;DR: In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception.
Journal ArticleDOI
Adapting to Unknown Smoothness via Wavelet Shrinkage
TL;DR: In this article, the authors proposed a smoothness adaptive thresholding procedure, called SureShrink, which is adaptive to the Stein unbiased estimate of risk (sure) for threshold estimates and is near minimax simultaneously over a whole interval of the Besov scale; the size of this interval depends on the choice of mother wavelet.