Abstract
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 adjus
Highlights
The rapid development of robot infrared technology has made infrared cameras more and more widely used in some areas such as automatic driving, night search and rescue, and security [1]
This paper proposes a robot infrared image enhancement method based on Retinex theory and modified non-local mean for the problem of robot infrared image contrast and detail enhancement
This paper proposes an infrared image detail enhancement method based on contourlet-based non-local mean and Retinex theory
Summary
The rapid development of robot infrared technology has made infrared cameras more and more widely used in some areas such as automatic driving, night search and rescue, and security [1]. Based on the histogram enhancement, these algorithms only pay attention to the global or local gray distribution, and do not consider the structural characteristics of the input image, which often causes the image visual effect to be stiff and the noise is enhanced Another type of image enhancement algorithm based on the spatial domain is adaptive image enhancement, including improved Unsharp masking method [10], Retinex [11], homomorphic filtering (HF) [12] and mathematical morphology [13]. This paper proposes a robot infrared image enhancement method based on Retinex theory and modified non-local mean for the problem of robot infrared image contrast and detail enhancement This method makes full use of Retinex, the adjustment effect of theory on image gray distribution and the good performance of probabilistic non-local mean filtering in image detail extraction have achieved good results in image contrast and detail enhancement
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