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Showing 2 results for Psnr
A New Shearlet Framework for Image Denoising
E. Ehsaeyan,
Volume 12, Issue 2 (6-2016)
Abstract

Traditional noise removal methods like Non-Local Means create spurious boundaries inside regular zones. Visushrink removes too many coefficients and yields recovered images that are overly smoothed. In Bayesshrink method, sharp features are preserved. However, PSNR (Peak Signal-to-Noise Ratio) is considerably low. BLS-GSM generates some discontinuous information during the course of denoising and destroys the flatness of homogenous area. Wavelets are not very effective in dealing with multidimensional signals containing distributed discontinuities such as edges. This paper develops an effective shearlet-based denoising method with a strong ability to localize distributed discontinuities to overcome this limitation. The approach introduced here presents two major contributions: (a) Shearlet Transform is designed to get more directional subbands which helps to capture the anisotropic information of the image; (b) coefficients are divided into low frequency and high frequency subband. Then, the low frequency band is refined by Wiener filter and the high-pass bands are denoised via NeighShrink model. Our framework outperforms the wavelet transform denoising by %7.34 in terms of PSNR (peak signal-to-noise ratio) and %13.42 in terms of SSIM (Structural Similarity Index) for ‘Lena’ image. Our results in standard images show the good performance of this algorithm, and prove that the algorithm proposed is robust to noise.


A Novel NeighShrink Correction Algorithm in Image Denoising
E. Ehsaeyan,
Volume 13, Issue 3 (9-2017)
Abstract
Image denoising as a pre-processing stage is a used to preserve details, edges and global contrast without blurring the corrupted image. Among state-of-the-art algorithms, block shrinkage denoising is an effective and compatible method to suppress additive white Gaussian noise (AWGN). Traditional NeighShrink algorithm can remove the Gaussian noise significantly, but loses the edge information instead. To overcome this drawback, this paper aims to develop an improvement shrinkage algorithm in the wavelet space based on the NeighSURE Shrink. We establish a novel function to shrink neighbor coefficients and minimize Stein’s Unbiased Risk Estimate (SURE). Some regularization parameters are employed to form a flexible threshold and can be adjusted via genetic algorithm (GA) as an optimization method with SURE fitness function. The proposed function is verified to be competitive or better than the other Shrinkage algorithms such as OracleShrink, BayesShrink, BiShrink, ProbShrink and SURE Bivariate Shrink in visual quality measurements. Overall, the corrected NeighShrink algorithm improves PSNR values of denoised images by 2 dB.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.