An impulse noise removal model algorithm based on logarithmic image prior for medical image

With the rapid development of computer science and technology in modern society, image science is widely used in various fields, especially in medicine field. Image processing plays an important role in medical images. Medical images are often corrupted by noise due to various sources of interference and other phenomena during their acquisition and transmission that affects the measurement processes in imaging reduced image detail due to the introduction of noise. Keeping useful diagnostic information to suppress noise is a challenging task. Salt and pepper noise as a kind of ordinary noise is one of the impulse noises. In this work, we will use a logarithmic image prior constraint the objective function for the removal of the impulse noise. Also, we used the split Bregman iterative method to solve the objective function. Theoretically, under reasonable assumptions, we give partial convergence analysis of the algorithm. Computationally, we use the split Bregman iterative method under the guarantee of convergence analysis and the weight of SVD decomposition; a complex problem is transformed into several simple subproblems to solving, wherein u-subproblem can be solved by fast Fourier transform; h, d-subproblems can be solved use shrinkage operator, respectively. In the experimental aspects, we have done a lot of experiments and compared with other state-of-the-art methods. The experimental results show that the method is superior to other methods in terms of effectiveness impulse noise (salt and pepper noise) for medical images (CT or MRI).