Enhancing Multispectral Vision: A GAN-Based Dehazing Framework for Improved Image Clarity

In the landscape of computational imaging, addressing atmospheric haze remains a persistent challenge, impacting the quality of images, especially in adverse weather conditions. This research presents an innovative strategy for multispectral dehazing, combining channel decomposition and Generative Adversarial Networks (GANs) to account for spectral differences linked to haze. The method involves the careful separation of several spectral channels, which allows for a more in-depth evaluation of the impact of the haze and more focused dehazing. Using the ability of GANs to extract fine details, the model is trained adversarially to learn and adjust to spectral variations caused by haze. The experimental results show significant performance compared to state-of-the-art dehazing models, as indicated by higher Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values. These striking outcomes highlight the effectiveness of the suggested approach and establish it as a very promising means of improving image quality over a variety of channels. This development will have a big impact on remote sensing and computer vision applications, where atmospheric haze is a major obstacle to visibility and accurate analysis.