Cross Fusion-Based Low Dynamic and Saturated Image Enhancement for Infrared Search and Tracking Systems

Unmanned aerial vehicles and battleships are equipped with the infrared search and tracking (IRST) systems for its mission to search and detect targets even in low visibility environments. However, infrared sensors are easily affected by diverse types of conditions, therefore most of IRST systems need to apply advanced contrast enhancement (CE) methods to cope with the low dynamic range of sensor output and image saturation. The general histogram equalization for infrared images has unwanted side effects such as low contrast expansion and saturation. Also, the local area processing for saturation reduction has been studied to solve the problems regarding the saturation and non-uniformity. We propose the cross fusion based adaptive contrast enhancement with three counter non-uniformity methods. We evaluate the proposed method and compare it with conventional CE methods using the discrete entropy, PSNR, SSIM, RMSE, and computation time indexes. We present the experimental results for images from various products using several datasets such as infrared, multi-spectral satellite, surveillance, general gray and color images, as well as video sequences. The results are compared using the integrated image quality measurement index and they show that the proposed method maintains its performance on various degraded datasets.