Sparse image denoising using dictionary constructed based on least square solution

摘要

Compressed sensing became a vital tool for image or signal reconstruction with less number of samples compared with the Nyquist rate. Among the existing algorithms for reconstruction of an image using compressed sensing, orthogonal matching pursuit algorithm is cost effective in terms of computational complexity. This algorithm provides a solution for overdetermined and underdetermined systems by minimizing the error functions using least square. This work concentrates on the construction of dictionary which can be used to solve the sparsity based image denoising problem. In this paper, we constructed the dictionary using least square solution subjected to thresholding conditions such as hard, soft and semi-soft. Orthogonal matching pursuit (OMP) algorithm avoids the selection of the same atom in every iteration, due to the existence of orthogonal property between the residue and the atom selected from the dictionary. Thus, OMP algorithm results in precise image reconstruction. The proposed method is validated on four standard test images, such as Lena, Boat, Barbara and Cameraman with different noises such as salt & pepper noise, Gaussian noise and speckle noise with varying the percentage of noise level from 5% to 40%. Obtained results are evaluated by the quality metric peak-to-signal-noise ratio (PSNR) and compared with the existing wavelet based sparse image denoising. The experimental evaluation shows that the proposed method is better applicable to remove the speckle noise and salt & pepper noise when compared with the existing wavelet based sparse image denoising.