Optimized weighted non-local mean filter for enhanced denoising and improved quality of medical images

Abstract

Image quality is significantly influenced by noise, light, and artifacts, particularly in medical images where precision is essential for accurate diagnosis. Denoising is a significant pre-processing for enhancing the overall quality of images to enable efficient classification, feature extraction, and segmentation. Conventional denoising filters smooth out boundaries and lose texture because they are ineffective to process color images. To address these limitations, a weighted factor-based non-local means (WF+NLM) filter is proposed as an improvement over the non-local means (NLM) filter, with an additional weight factor based on pixel similarity. This addition reduces blurring while maintaining fine details, resulting in improved quality. The proposed filter performs effectively in blood smear images, with a peak signal-to-noise ratio (PSNR) of 39.6904, SSIM of 0.9551, and gradient SSIM of 0.9889. Statistical tests indicates that the WF+NLM filter improves image quality in terms of structure, gradients, and feature similarity. Statistical inference for a one-tailed paired t-test validates statistical significance with the highest t value of 9.323829 with p-value 0.00037 by the wavelet-based non-local moment mean (W-NMM) filter asserts higher image restoration quality.