Contourlet-based non-local mean via Retinex theory for robot infrared image enhancement

Abstract

This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173789. 

Aiming at the problems of fuzzy details and excessive enhancement in traditional robot infrared image enhancement algorithms, a robot infrared image enhancement method based on Retinex theory and contourlet-based non-local mean is proposed. Firstly, the single-scale Retinex method is used to adjust the gray level of the over-dark and over-bright parts of the image. Then, the contourlet-based non-local mean is used to decompose the image to obtain the basic layer and detail layer. Histogram equalization is used to stretch the contrast of the basic layer, and nonlinear function is used to enhance the detail layer. Finally, the results of different levels are fused to obtain the contrast and detail enhanced robot infrared image. The proposed method is used to simulate several groups of robot infrared images in different scenes, and compared with other enhancement methods for subjective and objective analysis. The results show that the proposed method achieves better performance in detail and contrast enhancement of infrared images.