A Dual-Branch Attention Guided Context Aggregation Network for NonHomogeneous Dehazing

Abstract

Image degradation arises from various environmental conditions due to the exis tence of aerosols such as fog, haze, and dust. These phenomena mitigate image vis ibility by creating color distortion, reducing contrast, and fainting object surfaces. Although the end-to-end deep learning approach has made significant progress in the field of homogeneous dehazing, the image quality of these algorithms in the context of non-homogeneous real-world images has not yet been satisfactory. We argue two main reasons that are responsible for the problem: 1) First, due to the unbalanced information processing of the high-level and low-level information in conventional dehazing algorithms, 2) due to lack of trainable data pairs. To ad dress the above two problems, we propose a parallel dual-branch design that aims to balance the processing of high-level and low-level information, and through a method of transfer learning, utilize the small data sets to their full potential. The results from the two parallel branches are aggregated in a simple fusion tail, in which the high-level and low-level information are fused, and the final result is generated. To demonstrate the effectiveness of our proposed method, we present extensive experimental results in the thesis.