Color image denoising: a hybrid approach for mixed Gaussian and impulsive noise

This paper tackles the problem of mixed Gaussian and impulsive noise suppression in color images. The proposed method comprises two essential steps. Firstly, we detect impulsive noise through an approach based on the concept of digital path exploring the local pixel neighborhood. Each pixel is assigned a cost of a path connecting the boundary of a local processing window with its center. When the central pixel exhibits a high value of the path with lowest cost, it is identified as an impulse. To achieve this, we use a thresholding procedure for detecting corrupted pixels. Analyzing the distribution of minimum path costs, we employ the k-means technique to classify pixels into three distinct categories: those nearly undistorted, those corrupted by Gaussian noise, and those affected by impulsive noise. Subsequently, we employ the Laplace interpolation technique to restore the impulsive pixels — a fast and effective method yielding satisfactory denoising results. In the second step, we address the residual Gaussian noise using the Non-Local Means method, which selectively considers pixels from the local window that have not been flagged as impulsive. The experimental results confirm that our proposed hybrid method consistently yields superior outcomes compared to state-of-the-art denoising techniques. Moreover, its computational complexity remains low, rendering it suitable for real-time applications.