Image Enhancement Algorithm Research Based On Partial Differential Equations

Image processing is processing the image information to meeting people's visual psychology or application requirements. With the rapid development of the computer technology, image processing technology has been widely used in various fields. Image enhancement is one of the focus of the image processing, it is based on application requirements to stress out interesting features from the image, to achieve the purpose of improving the visual effects. Due to the random perturbation of electronic devices in the image acquisition device and the surrounding environment, making the images contains a variety of complex noise, leading to a certain distortion. Using image enhancement technology to improve the image, decay various types of noise, and highlight the target contour edges is significant to the follow-up image processing.This paper studies the image enhancement algorithm basing on theory of partial differential equations, analysises it's thinking and advantages by using the partial differential equation on image enhancement, and does deeply analysis for the classic Anisotropic Diffusion Model and the Total Variational Model. The paper proposed an improved enhancement algorithm based on the theories of P-M Model, Catte Model and FAB Model. The improved enhancement algorithm is using Catte Model in the flat area of images and using FAB Model at the edge of images. Another, the paper proposed an improved image enhancement algorithm through combining the Total Variation Model and the Shock Filter Model. The algorithm not only improves ladder effect of the total variation model, but also sharpes the image boundary. Through simulation experiments on different images, this paper proves the validity of the improved model by the peak signal to noise ratio.