Image Denoising Using a Modified LNMF Algorithm

A novel image denoising method is proposed in this paper by using local non-negative matrix factorization (LNMF) with sparse constraint, denoted by SC-LNMF. LNMF method can successfully extract the local feature of a nature image and denoise images efficiently. However, LNMF method does not consider the image's sparse prior distribution and the sparse control of feature basis vectors and sparse coefficients. To enhance the feature matrix's sparseness and the feature sub-space's locality, SC-LNMF is proposed here. Using 10 clear images to learn the SC-LNMF algorithm, simulation results show that this method is indeed efficient in extracting images' local features. Further, considering different noise variance, utilizing the SC-LNMF feature bases, the noise was reduced hardly. At the same time, using the signal noise ratio (SNR) measure to evaluate denoised images' quality, simulation results testify that our method proposed here is effective and feasible in performing image denoising task.