Edge-Aware Patch Grouping for Image Denoising in the Complex Wavelet Domain

摘要

This paper develops a new image denoising framework based on the Dual Tree Complex Wavelet Transform and an edge based patch grouping. The proposed patch grouping mechanism considers the photometric features along with gradient features to cluster the image patches into different groups with similar properties. Furthermore, the K-means algorithm was accomplished for patch grouping instead of Euclidean distance metric. An adaptive thresholding mechanism is also developed here to remove the noise with less information loss at edge features. Extensive simulation is carried out through MATLAB software over different grayscale images at different noise levels and noise types and the performance is measured with the performance metrics such as PSNR and SSIM for varying noise levels. The obtained simulation revealed the outstanding performance of proposed approach both in the preservation of edge features and also in the quality improvisation by efficient noise removal.