DefogNet: A Residual Network for Removal of Fog Using Weighted Combination Loss

Fog is a common atmospheric phenomenon that significantly affects outdoor images and videos, causing reduced visibility and image quality. Various image processing techniques have been proposed for fog removal, but most of them require prior knowledge about the scene or complex models, which can be computationally expensive. In this paper, we propose a deep learning-based technique called DefogNet for removing fog from images. DefogNet is a residual network that uses a weighted combination loss function to generate visually pleasing and perceptually accurate results. The network is trained end-to-end using a dataset of foggy and clear images, and it can efficiently restore the visibility of images without requiring any prior knowledge of the scene. Experimental results show that the proposed method outperforms state-of-the-art methods in terms of both quantitative metrics and visual quality. The proposed approach can be useful in various applications, including surveillance, self-driving cars, and outdoor photography.