Streamlined Global and Local Features Combinator (SGLC) for High Resolution Image Dehazing

• Main Authors: Benjdira, Bilel, Ali, Anas M., Koubaa, Anis
• Format: Conference Proceeding
• Language: English
• Subjects: Atmospheric modeling; Image resolution; Image synthesis; Measurement; Self-supervised learning; Streaming media; Training
• ISSN: 2160-7516
• DOI: 10.1109/CVPRW59228.2023.00184

Image Dehazing aims to remove atmospheric fog or haze from an image. Although the Dehazing models have evolved a lot in recent years, few have precisely tackled the problem of High-Resolution hazy images. For this kind of image, the model needs to work on a downscaled version of the image or on cropped patches from it. In both cases, the accuracy will drop. This is primarily due to the inherent failure to combine global and local features when the image size increases. The Dehazing model requires global features to understand the general scene peculiarities and the local features to work better with fine and pixel details. In this study, we propose the Streamlined Global and Local Features Combinator (SGLC) to solve these issues and to optimize the application of any Dehazing model to High-Resolution images. The SGLC contains two successive blocks. The first is the Global Features Generator (GFG) which generates the first version of the Dehazed image containing strong global features. The second block is the Local Features Enhancer (LFE) which improves the local feature details inside the previously generated image. When tested on the Uformer architecture for Dehazing, SGLC increased the PSNR metric by a significant margin. Any other model can be incorporated inside the SGLC process to improve its efficiency on High-Resolution input data.