Adaptive Image De-noising Algorithm in Intersecting Cortical Model

Abstract

Because the classic intersecting cortical model (ICM) and the traditional image de-noising algorithm exist the deficiencies-the image collection, transmission and conversion are often subjected to impulse noise interference, thus affecting the quality of the image, therefore we improved the framework structure and related parameters of the ICM and proposed the adaptive image de-noising algorithm. Through improving the intersecting cortical model (ICM), we use the timed matrix information of the improved IICM to determine the specific location of the pixels polluted by the impulse noise, and then use the adaptive image de-noising algorithm to complete the de-noising process of the noise pixels on the basis of the improved IICM structure. Finally we made a detailed experimental validation and comparison. The experiments show that the improved intersecting cortical model (ICM) and the adaptive image de-noising algorithm have the superior impulse noise filter performance than the classic de-noising algorithm and can improve the image quality.