Adaptive Infrared Images Enhancement Using Fuzzy-Based Concepts

Image enhancement is the process of modifying digital images so that results are suitable for human perception. An upcoming need for image visualization during all lighting conditions by the use of infrared (IR) imagery has gained momentum. It is deemed fit for efficient target acquisition and object deduction. However, due to low image resolution and difficulty in spotting certain objects whose temperature is similar to that of the ground, infrared images must be subjected to further enhancement. Our given proposal aims to enhance infrared images, making use of the fuzzy-based enhancement technique (FBE), and to compare its efficacy with other techniques such as histogram equalization (HE), adaptive histogram equalization (AHE), max–median filter, and multi-scale top-hat transform. The enhanced image is then analyzed using different quantitative metrics such as peak signal-to-noise ratio (PSNR), image quality index (IQI), and structural similarity (SSIM) for performance evaluation. From experimental results, it is concluded that FBE results in the best quality image.