An approach to adaptive infrared image enhancement for long-range surveillance

This paper presents an adaptive image enhancement approach to infrared (IR) images for long-range surveillance. The IR images captured at long range usually have low contrast, low brightness, and small hot objects of interest. The main characteristics of the proposed approach are that no prior knowledge about the IR image is necessary and no parameters must be preset. Two main goals are sought: adaptive contrast enhancement and enhancement of the power of high spatial frequency in IR images. In our work, a novel adaptive histogram-based equalization (AHBE) is used for adaptive contrast enhancement. It can automatically generate a hybrid cumulative histogram (HCH) based on two different pieces of information about the image histogram. In this way, the proposed adaptive equalization approach can improve the enhancement effect on small hot objects embedded in an image. In addition, using a high-boost filter enhances the power of high spatial frequency in IR images and maintains the information about the original images. As a result, the diffraction effects on IR images caused by the IR optical system will be ameliorated through the high-boost filter. Experimental results demonstrate that the proposed approach is feasible for use as an effective and adaptive process to enhance the quality of IR images, regardless of the hot objects’ sizes. Our findings will be helpful in applying IR images to long-range surveillance.

Research highlights
► We propose an adaptive histogram-based equalization (AHBE) to contrast enhancement.
► The AHBE can automatically generate a hybrid cumulative histogram (HCH).
► The AHBE has a greater enhancement effect on small hot objects than on backgrounds.
► We apply a high-boost filter to enhance the high spatial frequency in IR images.