Contrast enhancement algorithm for infrared images based on adaptive morphological reconstruction and multi-curve Gamma correction using histogram features

The visual quality of infrared images is usually affected by low contrast, blurring, and noise interference. Existing enhancement methods exhibit certain limitations in simultaneously improving contrast, enhancing detail information, and suppressing noise. To overcome these shortcomings, this study proposes an infrared image contrast enhancement algorithm based on multi-scale adaptive morphological reconstruction and multi-curve Gamma correction with histogram features. First, the multi-scale adaptive grayscale morphological reconstruction (MSAGMR) method is employed to suppress noise and smooth the image while preserving edge contours, thereby extracting the base layer of the image. Subsequently, global contrast adjustment is performed on the base layer using an improved Gamma correction algorithm. Subsequently, the detail information of the image is extracted without amplifying the noise by the difference in multiscale adaptive gray morphology reconstruction (DoMSAGMR) method, and the detail components are enhanced by combining the visual properties of the human eye with the Laplace operator. Finally, a high-quality infrared image is generated by fusing the enhanced base layer and the detail layer. Experimental results demonstrate that the proposed algorithm significantly improves visual contrast and detail representation while exhibiting good noise suppression capability. Experimental results show that the proposed algorithm outperforms the existing related methods, achieving excellent contrast enhancement, especially for complex infrared images that are disturbed by noise, overexposed, or underexposed.