An Improved Denoising Algorithm for Removing Noise in Color Images
Sasi Kala Rani, Y. Chabrra, Kamal Malik
Abstract
Noise has a significant impact on image quality in a variety of applications, including machine vision and object recognition. Denoising is crucial for successful image processing since noisy pictures lead to erroneous findings and segmentation and enhancement mistakes. Existing methods were primarily developed for grayscale image denoising and are unable to detect all damaged pixels in an image effectively. This paper proposes a sequential ROAD-TGM-HT method to suppress impulsive noise in color image denoising. The noisy pixel location is detected using the consecutive method in the first step, and the distorted value of the damaged pixel is reconstructed in the second stage, followed by the Hough transform for the remaining undetected pixels. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) were used to analyze the qualitative and quantitative performance. ROAD-TGM-HT excels on color images with noise levels ranging from 0.10 to 0.70, as per PSNR and SSIM qualitative data.