Image Denoising by using Modified SGHP Algorithm
TL;DR: A perspective that is modified parameter in S-Gradient Histogram Preservation denoising method is proposed and a measure of structure gradient histogram of a given image is measured.
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Abstract: In real time applications, image denoising is a predominant task. This task makes adequate preparation for images looks prominent. But there are several denoising algorithms and every algorithm has its own distinctive attribute based upon different natural images. In this paper, we proposed a perspective that is modified parameter in S-Gradient Histogram Preservation denoising method. S-Gradient Histogram Preservation is a method to compute the structure gradient histogram from the noisy observation by taking different noise standard deviations of different images. The performance of this method is enumerated in terms of peak signal to noise ratio and structural similarity index of a particular image. In this paper, mainly focus on peak signal to noise ratio, structural similarity index, noise estimation and a measure of structure gradient histogram of a given image.
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References
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.
17.3K
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.
9.6K
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Michael Elad,Michal Aharon +1 more
TL;DR: This work addresses the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image, and uses the K-SVD algorithm to obtain a dictionary that describes the image content effectively.
6.2K
Nonlocally Centralized Sparse Representation for Image Restoration
TL;DR: The so-called nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, and the extensive experiments validate the generality and state-of-the-art performance of the proposed NCSR algorithm.
Image denoising: Can plain neural networks compete with BM3D?
Harold Christopher Burger,Christian J. Schuler,Stefan Harmeling +2 more
- 16 Jun 2012
TL;DR: This work attempts to learn this mapping directly with a plain multi layer perceptron (MLP) applied to image patches and shows that by training on large image databases it is able to compete with the current state-of-the-art image denoising methods.
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