Contrast Enhancement Algorithm for Outdoor Infrared Images Based on Local Gradient-Grayscale Statistical Feature

Contrast enhancement for infrared images is important in various night vision applications. However, existing local contrast enhancement algorithms often over-enhance smooth regions in outdoor infrared images. To address this limitation, this paper presents a contrast enhancement algorithm based on local gradient-grayscale statistical feature. The proposed algorithm first extracts such features from image sub-blocks, then classifies the sub-blocks as either simple or non-simple based on textural complexity using a model trained by a support vector machine, and subsequently adopts different grayscale mapping strategies to process the two types separately. Experimental results show that the proposed algorithm avoids overenhancing simple regions while effectively improving the contrast in regions with more details.