Entropy-based Adaptive Image Denoising
Long Ye, Haijun Gao, Qin Zhang · Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) · 2013
Image entropy, which not only describes the average amount of information about the image source, but also reflects the statistical characteristics of image data, can be described as the properties of image features and image processing basis.This paper is aimed to put forward an adaptive image denoising solution by setting the threshold and analyzing the impact of the original image data, which comes from the image noise under different entropy.For the blocks in image with low entropy value, we apply Haar wavelet method to denoise; for the medium entropy, we use field of expert (FoE) model as the prior information of the medium region denoising to achieve the image denoising of these blocks, and for the high entropy blocks, because little difference existed between ideal and noised image in human perception, no processing is implemented in this kind of blocks.The adaptability of our framework is embodied in the adjusting of the thresholds to classify different entropy blocks.Experiment results demonstrate the advantages of our framework measured by both PSNR and SSIM.