Texture-oriented image denoising technique for the removal of random-valued impulse noise

Abstract

An iterative texture-oriented image denoising technique is proposed for the restoration of images corrupted with random-valued impulse noise (RVIN). The proposed technique opts a switching approach that first identifies the pixels corrupted by RVIN and then estimates their intensity values to restore the images. Textons of distinct orientations conforming to bilateral symmetry are proposed for the identification of corrupted pixels. To estimate the intensity values of the identified corrupted pixels, the textons having local similarity are used. As the textons are fundamental elements of texture perception, the proposed technique preserves the texture information of images, effectively. The performance of the proposed denoising technique is evaluated on standard benchmark test images under various intensities of RVIN by comparing it with state-of-the-art techniques. The simulation results depict the significant performance of the proposed denoising technique for low as well as higher intensities of RVIN.