Multiplicative decomposition based image contrast enhancement method using PCNN factoring model
Guangzhu Xu, Chunlin Li, Jingjing Zhao, Bangjun Lei · 2014
Contrast enhancement helps human perceive and analyze low level quality images and has an important role in image processing applications. Being different from traditional methods based on histogram processing, this paper explored an image contrast enhancement technique based on multiplicative decomposition. With improved pulse coupled neural network factoring model (PCNN-FM) specially designed for contrast enhancement application, images with different details were output as multiplicative factors. Initial input image could be rebuilt almost without deviation by multiplying these factors. So enhancing factor would improve corresponding image details. Based on this point, the paper divided initial image with the first factor output by proposed PCNN-FM to implement contrast enhancement. This processing equals rebuilding initial image with factor images by maximizing the first factor. Experiments show that the dynamic range of enhanced image is moderate, and detail information is rich. In addition, the proposed method is universal, easy to implement and has actual using value.