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.

翻译：图像去雾旨在去除图像中的大气雾霾。尽管近年来去雾模型取得了长足发展，但鲜有模型能精准处理高分辨率雾霾图像问题。对于此类图像，模型需在降采样版本或裁剪图像块上运行，这两种方式都会导致精度下降。这主要源于图像尺寸增大时，全局与局部特征融合的固有失效。去雾模型需要全局特征来理解整体场景特性，同时需要局部特征以更好处理精细像素细节。本研究提出流式全局与局部特征组合器(SGLC)来解决上述问题，并优化任意去雾模型在高分辨率图像上的应用。SGLC包含两个连续模块：首个模块为全局特征生成器(GFG)，用于生成富含全局特征的初始去雾图像；第二个模块为局部特征增强器(LFE)，用于改善前序生成图像中的局部特征细节。在基于Uformer架构的去雾测试中，SGLC使PSNR指标获得显著提升。任意其他模型均可集成至SGLC流程中，以提升其对高分辨率输入数据的处理效能。