Video Denoising Based on SWT & DWT Implemented with Soft Shrinkage Rule

Video denoising is an important step in image and video processing applications. Digital images are corrupted by various types of noise during acquisition and transmission. In this paper, an approach for video / image frame denoising based on Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT) for Additive White Gaussian Noise (AWGN) removal is proposed. The noise is minimized by both hard and soft thresholding of high frequency sub-bands of SWT and DWT, followed by bicubic interpolation of high frequency sub-bands of DWT and SWT. This result in modified noise free higher order subbands. The results obtained on wide range of noise corruption (up to 40%) are shown and discussed. Moreover, comparison with wellestablished methods for AWGN removal is also provided. Obtained results reveal that the proposed algorithm outperforms other approaches of AWGN removal and its performance is close to optimality. The proposed algorithm can significantly improve the visual quality of the noisy video / image frame by maintaining sharp edges, and clearly smooth out noise from most parts of the image frame.