A New Approach for Bayesian Denoising in Images Using an Object Homogeneity Prior
Hansun Kim, Cheolkon Jung, Sunghyun Choi, Sangseop Lee, Joong Kyu Kim · ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications · 2009
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.