Super-efficient enhancement algorithm for infrared night vision imaging system

Abstract

This paper presents new three proposed approaches for enhancement of Infrared (IR) night vision images. The first approach is based on merging gamma correction with the Histogram Matching (HM). The second approach depends on hybrid gamma correction with Contrast Limited Adaptive Histogram Equalization (CLAHE). The third approach is based on a trilateral enhancement that the IR images pass through three stages: segmentation, enhancement and sharpening. In the first stage, the IR image is divided into segments based on Optimum Global Thresholding (OTSU) method. The second stage, which is the heart of the enhancement approach, depends on Additive Wavelet Transform (AWT) to decompose the image into an approximation and details. Homomorphic enhancement is performed on the detail components, while Plateau Histogram Equalization (PHE) is performed on the approximation plane. Then, the image is reconstructed and subjected to a post-processing high pass filter. Average gradient, Sobel edge magnitude, entropy and spectral entropy has been used as quality metrics for assessment the three proposed approaches. It is clear that the third proposed approach gives superior results to the two proposed approaches point view the quality metrics. On the other hand, clear that the third proposed approach takes long computation time in the implementation with respect to the two proposed approaches. The first proposed approach gives better results to the two proposed approaches from the computation time perspective.