Maximum a Posteriori Based (MAP-Based) Video Denoising VIA Rate Distortion Optimization
In this paper, a maximum a posteriori based (MAP-based) video denoising algorithm is proposed. According to the Bayes rule, the MAP estimate is determined by two terms: noise conditional density model and priori conditional density model. Based on the assumptions that the noise satisfies Gaussian distribution and the priori model is measured by the bit rate, the MAP estimate can be expressed as a rate distortion optimization problem. In order to find a suitable Lagrangian parameter, we re-write the problem as a constraint minimization problem by setting the rate as an objective function and the distortion as a constraint. In this way, we find that the Lagrangian parameter is determined by the distortion constraint. Fixing the distortion constraint, we can get the optimal Lagrangian parameter, which leads to the optimal denoising result. Some experiments are conducted to demonstrate the efficiency and effectiveness of the proposed method.