Digital Image Restoration Using NL Means with Robust Edge Preservation Technique
We present a novel approach for image denoising with spatial domain edge preservation method to yield denoised image, from an image which is distorted by additive white Gaussian noise without loss of detail information of an image. Denoising of noise corrupted images while preserving its attributes like edges and fine details is an extreme challenge. During image acquisition and transmission often noise gets added in the image which degrades the image quality. Algorithms like NL means and BM3D have been very successful in this aspect. The weight assigned to the fellow pixels for calculating replacement for a noisy pixels have a pivotal role in these algorithms. In NL means, the weight assigned to a pixel depends only on its relative magnitude with respect to its neighborhood. In this paper we are proposing a robust mechanism for weight calculation which also considers the orientation of a pixel. Using this mechanism we got improvement in PSNR and visual quality of the de-noised image as compared to the NL means method and also got improvement in structural similarity as compared to the BM3D. From the experimental results it can be seen that our proposed algorithm is superior in comparison to the NL means and BM3D technique of image denoising, considering the factors like PSNR, SSIM and accordingly conclusions are drawn.
KeywordsNon-local means (NL means) BM3D algorithm Gaussian noise weight calculations Weighted variance PSNR and SSIM
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