Gray level DAG maximum entropy based on quantization resolution for Medical image tone enhancement

Abstract:

In order to improve the medical image sharpness and contrast, and improve the computational efficiency, we proposed the gray level DAG maximum entropy based on quantization resolution for Medical image tone enhancement. Firstly, we used a simple piecewise autoregressive (Piecewise autoregressive PAR) image target model for recovery, and taked into account the error of analog to digital conversion to use least squares algorithm to estimate PAR model parameter, which obtain high resolution image histogram restoration model; Secondly, aiming at the problem of low contrast may exist, the least squares algorithm for constrained optimization problems was modeled in DAG, which constructed a hue preserving constraint optimization model of maximum entropy image enhancement, and the characteristics of the DAG figure Monge theorem was used to reduce the computational complexity; Through the above two steps, the image details and contrast enhancement in the process of medical image enhancement are realized. The simulation results show that the proposed algorithm can provide more effective medical image enhancement effect.