Image denoising using RANSAC and compressive sensing

Image denoising is a vital image processing phase aiming to improve the quality of images and to make them more informative. In this paper, we propose a blind denoising approach for removing the outliers (impulsive disturbances) from digital images, by combining the random sample consensus (RANSAC) and compressive sensing (CS) principles. The proposed approach exploits the fact that images are highly concentrated in the domain of two-dimensional discrete cosine transform (2D-DCT). The sparsity (high concentration) in the transform domain is used in both detection and reconstruction of pixels affected by high disturbances. The image pixels not affected by the noise are found using the RANSAC-based methodology and they are further used as available measurements in the CS reconstruction. The affected pixels are considered unavailable and they are recovered by the CS procedure. The presented approach does not require any disturbance-related assumptions regarding the statistical behavior of the noise or about the range of its values. The theory is verified on examples with 55 images. The comparative analysis against several state-of-the-art methods, done with full-reference and no-reference quality metrics, suggests that the proposed method can be used as an efficient tool for image denoising.