Image dehazing is an ill-posed low-level computer vision problem that substantially affects the reliability of outdoor vision systems, including autonomous driving, intelligent surveillance, remote sensing, and aerial imaging. Atmospheric degradations such as haze, fog, smoke, and rain scatter incoming light, resulting in poor visibility, contrast degradation, and color distortion. Although recent deep learning-based approaches, particularly, Generative Adversarial Networks (GANs), have shown promising performance in single image dehazing, most existing methods rely on a single convolutional generator and often suffer from generalization, artifacts, and color distortion. This paper presents a multi-CNN-GAN-based framework for effective image dehazing overcoming the existing limitations. The framework is designed to combinedly handle three salient issues, such low-level haze characteristics, mid-level transmission-aware representations, and high-level semantic consistency through three parallel CNN branches and feature fusion for realistic dehazing. In addition, a detailed experimental design for practical implementation along with future works has been provided. The proposed framework shows a way for next-generation image dehazing systems.
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