An Efficient Image Denoising Approach for the Recovery of Impulse Noise
S. Rajkumar,G. Malathi +1 more
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TLDR
A hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images and the performance of the proposed image enhancement scheme is proved using the advanced performance metrics.Abstract:
Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image’s pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics.read more
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References
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TL;DR: In this article, a hybrid method that combines phase preserving, Richardson Lucy deblurring and point spreading function (PSF) deblurings was proposed to restore astronomy images.
Proceedings ArticleDOI
Impulse Noise Removal from Gray Scale Images Based on ANN Classification Based Fuzzy Filter
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Proceedings ArticleDOI
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Proceedings ArticleDOI
A blind image deconvolution method based on noise variance estimation and blur type reorganization
Chong Yi,Tetsuya Shimamura +1 more
TL;DR: A novel blind image deconvolution approach based on noise variance estimation and blur type reorganization is presented and the restored image is obtained with least-square filter.
Proceedings ArticleDOI
A new total variation based image denoising and deblurring technique
TL;DR: A new total variation based de-noising scheme that optimally finds the threshold level of the noisy image wavelet decomposition that minimizes the energy of the error between the restored and the noisy images.