Infrared images have their own characteristics: low contrast, great noise, large dynamic range and poor visual effect. The traditional image enhancement algorithms have certain limitations and can't achieve a good visual effect. In order to obtain a good visual effect and improve the target detection and recognition capabilities, the paper studied various enhancement methods. After analyzing the retinex theory, we choose the image enhancement method based on human visual system called retinex to process infrared images. Retinex has been used to enhance the visible light image. To do experiment on infrared image enhancement, multi-scale retinex method gets ideal visual effect. On this basis, we propose an improved multi-scale Retinex (AMSR) method based on adaptive adjustment. This method can adaptively adjust the gray level and contrast of the image, enhance the details, make the weak small targets more conducive to the human eye observation. While, it is impossible to find a method suited for all infrared images with different characteristics. So, we use several traditional image enhancement algorithms to compare with the retinex algorithms. And calculate the objective evaluation factors, including average, standard deviation, entropy and so on. After observation the processing results and analyzing these evaluation factors, the AMSR algorithm is proved having its applicability and superiority. In order to select a suitable infrared image enhancement algorithms, we analyze the applicability of each enhancement methods for infrared image has obvious characteristics, To some extent, the study is significant to the infrared target detection and recognition.

Date Published: 24 November 2014
PDF: 7 pages
Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93013C (24 November 2014); doi: 10.1117/12.2073170

Published in SPIE Proceedings Vol. 9301:
International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition
Gaurav Sharma; Fugen Zhou; Jennifer Liu, Editor(s)