TrMLGAN: Transmission MultiLoss Generative Adversarial Network framework for image dehazing

Abstract

Hazy environments significantly degrade image quality, leading to poor contrast and reduced visibility. Existing dehazing methods often struggle to predict the transmission map, which is crucial for accurate dehazing. This study introduces the Transmission MultiLoss Generative Adversarial Network (TrMLGAN), a novel framework designed to enhance transmission map estimation for improved dehazing. The transmission map is initially computed using a dark channel prior-based approach and refined using the TrMLGAN framework, which leverages Generative Adversarial Networks (GANs). By integrating multiple loss functions, such as adversarial, pixel-wise similarity, perceptual similarity, and SSIM losses, our method focuses on various aspects of image quality. This enables robust dehazing performance without direct dependence on ground-truth images. Evaluations using PSNR, SSIM, FADE, NIQE, BRISQUE, and SSEQ metrics show that TrMLGAN significantly outperforms state-of-the-art methods across datasets including D-HAZY, HSTS, SOTS Outdoor, NH-HAZE, and D-Hazy, validating its potential for real-world applications.