Enhancement of Infrared Imagery through Low-Light Image Guidance Leveraging Deep Learning Techniques

Addressing challenges in infrared imaging, such as low contrast, blurriness, and detail scarcity due to environmental limitations and the target's limited radiative capacity, this research introduces a novel infrared image enhancement approach using low-light image guidance. Initially, the Cbc-SwinIR model (coordinate-based convolution- image restoration using Swin Transformer) is applied for super-resolution reconstruction of both shimmer and infrared images, improving their resolution and clarity. Next, the MAXIM model (multiaxis MLP for image processing) enhances the visibility of low-light images under low illumination. Finally, the AILI (adaptive infrared and low-light)-fusion algorithm fuses the processed low-light image with the infrared image, achieving comprehensive visual enhancement. The enhanced infrared image exhibits significant improvements: a 0.08 increase in fractal dimension (FD), 0.094 rise in information entropy, 0.00512 elevation in mean square error (MSE), and a 12.206 reduction in peak signal-to-noise ratio (PSNR). These advancements in FD and information entropy highlight a substantial improvement in the complexity and diversity of the infrared image's features. Despite a decrease in PSNR and an increase in MSE, this indicates that the newly introduced information enhances contrast and enriches texture details in the infrared images, resulting in pixel-level variations. This methodology demonstrates considerable improvements in visual content and analytical value, proving relevant, innovative, and efficient in infrared image enhancement with broad application prospects.