An Intelligent Impulse Denoising Technique Using Sequential Region Expansion

This study proposes an intelligent impulse denoising technique that utilizes sequential region expansion to address the limitations of traditional denoising methods such as mean and median filters. The proposed algorithm selectively identifies noisy pixels and adaptively expands the filtering region based on local noise density. This adaptive approach minimizes the processing of non-noisy pixels, thereby preserving important image details and maintaining visual fidelity. Extensive experiments on grayscale and color images validate the effectiveness of the proposed method. TheBR/algorithm shows improved performance in noise reduction and detail preservation compared to conventional techniques, including adaptive and weighted median filters. Quantitative evaluations using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics demonstrate reliable performance across varying noise intensities, maintaining high image quality.