Generalized variation-based regularization method for infrared image denoising

A generalized variation (GV) regularization based infrared image denoising method was proposed in this paper. In the new method, a p-norm was used as regularized term to replace total variation (TV) norm in traditional TV based image denoising methods which were used popular in image processing domain. Then a smoothing functional was constructed for noised removal. Thus, the problem of image denoising was transformed to a problem of a functional minimization. A nonlinear partial differential equation (PDE) was deduced from the new image denoising model. To solve the nonlinear PDE, the fixed point iteration (FPI) scheme was introduced to linear the PDE. The stability and convergence of regularized solution were ensured by FPI scheme. The numerical experimental results show that comparison with TV regularized method, the GV regularized method can preserve image edge including those small width edges more efficiently while removing noise. The GV regularized method is an efficient image noise removed method with better performance of noise removal and edge preserving.