Abstract
Background/Objectives: Image denoising is an important step in image processing applications. Usually noise is added to the original image during transmission, acquisition and storage process and is considered as noisy image. For precise analysis and extraction of image features, the noisy image is denoised without losing the original image details. This study aims to introduce a novel denoising method to obtain denoised image(s) such that it has fewer artifacts and is more efficient at higher noise levels. Method: The proposed novel denoising method introduces Adaptive Non Local Means (ANL) along with Method Noise Thresholding (MNT) technique to improve the image quality of the denoised image. Method Noise (MN) image obtained by taking the difference of image details between noisy image and pre-filtered mage. Recovered value from the MN through thresholding includes some of the important components of the original image. These values computed added to pre-filtered image to recover image features of the original image. Findings: The standard image, denoised with noise standard (s =10) using bior6.8 wavelet when filtered using existing Gaussian Bilateral Filter along with Method- Noise Thresholding filtering technique and Wiener Filter along with Residual Thresholding show improvement in quality of the denoised image in terms of PSNR and ISSN values as compared to the proposed filtering technique. The proposed filter technique results in higher PSNR and ISSN values (PSNR =33.80 and SSIN =0.9994). Novelty: It is known that ANLM results in improved denoised parameters compared with NLM filter; however, when MNT is blended with ANLM shows further improvement in quality of denoised image. Hence, in the proposed method, MNT is incorporated along with ANLM for improvement in denoising process. Image Quality Index (IQI) of the different standard images using ANLMT filtering technique is also studied. Keywords: Adaptive NonLocal Means filter; Gaussian Filter; Method Noise; Wavelet Thresholding; WienerFilter; and Adaptive Non Local Means Filter with Method Noise Thresholding
Highlights
Image processing is in practice in most of the medical, industrial, and military applications
A standard 256 x 256 grayscale images denoised using Wavelet Thresholding (WT), Gaussian Bilateral Filter with Method Noise Thresholding (GBFMT) and Weiner Filter with Residual noise Thresholding (WFRT) methods is compared with proposed denoising algorithm
The method noise thresholding technique incorporated along with Adaptive Non Local Means (ANL) filter in the present study show better Peak Signal to Noise Ratio (PSNR) as compared to GBFMT and WFRT methods
Summary
Image processing is in practice in most of the medical, industrial, and military applications. In this process, for accurate analysis of the image(s) by human interpretation or autonomous machine perception, denoising of the image is mandatory. Denoising process helps to obtain original image from the corrupted image. Continuous efforts made by the researchers in this field to improve coding technique or introduce new filtering methods to get better-denoised images in terms of retaining or recovering original details of the image. Improved Non Local Means (NLM) and other denoising techniques proposed in recent years (1–4)
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