  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.

Computer Vision and Pattern Recognition