Medical image denoising using sparse representations
Image noise reduction, or denoising, became an attractive research domain in the recent years. There are several published approaches in the image denoising field and each one has its presumptions, advantages, and limitations. In this paper, we address the problem of eliminating or even decreasing the noise from medical images. Some schemes for image noise reduction are proposed. The schemes include DCT_OMP, DCT_BOMP, Log Gabor_BOMP, DCT_OCMP and Wavelet_OMP. In these schemes, the image content is represented as a sparse linear combination of a set of atoms that can be obtained from some trained dictionaries. In literature, several algorithms are proposed to build up these kinds of dictionaries. In our study, the K-SVD algorithm is utilized to obtain a dictionary that can effectively describe the image. Also, some greedy algorithms are used to perform the sparse-coding of the signal. The proposed schemes have been tested on two different medical images with different levels of an additive noise. The Log Gabor_BOMP scheme showed a significant outperformance, in both the de-noising results and computational time, on all the other schemes.