Clustering-based natural image denoising using dictionary learning approach in wavelet domain

The existence of noise is inevitable in real-world applications of digital image processing. Theoretically, image restoration is the process to recover high-quality images from noisy images using adequate techniques. One of the pivotal applications of natural image restoration is the noise reduction (denoising). In this study, clustering-based natural image denoising using dictionary learning algorithm in wavelet domain is proposed (CDLW). This algorithm is exploiting the second-generation wavelet clustering coefficients in the decomposition levels. The use of second-generation wavelet transform in the proposed algorithm has its purposes which promotes the sparsity and multiresolution representations. The significance of second-generation wavelet transform is utilized in order to promote the hierarchical property of CDLW, while sparsity with self-similarity of the image source is utilized to connect the clustered coefficients. Extensive experiments have been conducted in order to show the objective and subjective competitive performance and have shown convincing improvements over the best state-of-the-art denoising methods. Inspired by the nonlocal features of the proposed algorithm structure, the time complexity comparisons showed that CDLW has the most efficient performance in the execution time compared to the rest of algorithms under investigation.