Joint Contrast Enhancement and Exposure Fusion for Real-World Image Dehazing

Due to the complexity of real environment and potential defects of current simulation datasets, either prior-based or deep learning-based single image dehazing methods may not work well in certain scenarios. In this work, we propose an efficient joint contrast enhancement and exposure fusion (CEEF) framework to formulate image dehazing task as a problem of enhancing local visibility and global contrast. In the contrast enhancement stage, several intermediate images are generated through two pre-processing steps. Specifically, gamma correction (GC) is used to adjust local visibility of an input hazy image. To address the issue of applying adaptive histogram equalization (AHE) to each color channel independently, we introduce color-preserving AHE (CP-AHE) to improve global contrast of the input hazy image. In the fusion stage, we develop a fast structural patch decomposition-based fusion strategy with an adaptive kernel size to fuse the inputs obtained by GC and CP-AHE. Extensive experiments on the real-world datasets demonstrate superiority of the proposed method to state-of-the-art methods in terms of visual and quantitative evaluation. Particularly for nighttime hazy scenes, our approach is shown to retain fine details and reduce color artifacts against three latest nighttime defogging methods. Moreover, we discuss potential applications of our CP-AHE in low-light enhancement and image editing.