Thermal image enhancement by artificial multiscale-exposure image fusion

Abstract

Infrared and thermal images have been used widely in different security applications. One of the drawbacks of such images is low contrast and noisy images, which should be enhanced. We present a new image enhancement algorithm based on block-rooting processing with artificial multi-scale-exposure image fusion. The proposed block-based multi-scale enhancement method is based on a 3-D block-rooting transform domain technique comprised: finding similar blocks in the image by block-matching; block-grouping for different block sizes; applying 3-D block-matching image enhancement; decomposition of the weight map and multi-scale enhanced images into the Gaussian and Laplacian pyramids; fusion by multiplying multi-scale images and weights. A new stage is proposed to obtain a local-global estimate of high-contrast images, also used in the general artificial fusion model. Some presented experimental results illustrate the performance of the proposed method on the thermal image dataset compared with the traditional methods.