Adaptive window-based filter for high-density impulse noise suppression

One of the primary factors that lower image quality is noise. In the world of image processing, image denoising is a crucial image-enhancing operation. This paper introduces and presents an improved denoising technique based on an adaptive ROAD-TGM-HT (rank order absolute difference-trimmed global mean Hough transform) filter that effectively eliminates high-density salt and pepper noise. The proposed approaches are evaluated using the MATLAB program. In qualitative and quantitative statistical analyses, performance metrics such as the Peak Signal to Noise Ratio, Image Enhancement Factor, and Structural Similarity Index have been utilized. Performing image denoising using these performance criteria. Grayscale and color pictures are utilized to test the Proposed Algorithm (PA), which offers an exceptional PSNR and IEF, especially at rising noise densities. This method considerably enhances image quality in comparison to prior techniques by efficiently reducing high-density noise produced during image acquisition or resulting from data transfer. To overcome this issue, we strengthened the ROAD-TGM by making it adaptable, and for the remaining pixels, we added the Hough Transform method, allowing it to perform better in scenarios with a high noise density.