A New Approach for Bayesian Denoising in Images Using an Object Homogeneity Prior

Images obtained from devices such as telephony devices, web cams etc. are inherently affected by noise. The proposed method applies a Bayesian framework for efficient denoising of images corrupted with noise which can, respectively, be used as a backbone to effectively reducing much more complex noise. A concept expressing the image as an energy function and appropriating a Bayesian framework is explained and consequently, an object homogeneity prior is employed to find the maximum a posteriori (MAP) estimator. An optimization method introduced is implemented to cogently reduce computation time. Experimental results are compared with conventional prior terms and we quantify the achieved performance improvements.