Contrast enhancement algorithm for infrared images based on multiscale difference of morphological reconstruction

The visual interpretation of infrared images is often hindered by low contrast and a limited dynamic range. This poses a significant challenge as existing enhancement methods are often limited in their ability to simultaneously improve contrast, suppress noise, and effectively handle overexposed and underexposed regions. To overcome these limitations, this article presents a novel contrast enhancement algorithm for infrared images based on the multiscale difference of morphological reconstruction. The proposed algorithm operates in three steps. First, a sequential gray morphological reconstruction (SGMR) technique is introduced to effectively remove noise by eliminating bright and dark connected regions in the image. Second, the difference of sequential gray morphological reconstruction (DoSGMR) is designed to extract salient information at various scales, enabling the enhancement of details without amplifying noise. Third, an adaptive fusion strategy is designed to integrate the extracted salient information, enhancing the contrast while simultaneously addressing overexposure and underexposure problems. Experimental results demonstrate that the proposed algorithm outperforms existing methods, achieving superior contrast enhancement, particularly for challenging infrared images corrupted by noise, overexposure, or underexposure.