Dehazing with all we have

In the near past, a large number of classical intuitively originated dehazing and image enhancing approaches have been worked out, and once played key roles in tremendous practical application scenes. Nevertheless, nowadays, the booming of deep neural networks has fundamentally overturned the entire society, and deep learning is widely believed as the main dominant SOTA dehazing framework. Here, we wonder does that imply those once shining intuitive approaches are totally outdated and useless anymore? Following this idea, we propose a general framework that takes full advantage of both traditional intuitively designed and modern deep data driven series of techniques to realize high-quality image dehazing. It is mainly composed of two stages: Multiple I-Filters based Knowledge Extraction (MIF-KE) and Multi-Complexity Paths based Knowledge Analysis and Fusion (MCP-KAF). In MIF-KE, diverse intuitive dehazing techniques are sufficiently explored and evolved to a bunch of adaptive content enhancing I-Filters (I for Intuitive), with the assistance of automatic deep bilateral learning. Then, through pixel-wise affine transformation, these filters are imposed on preliminarily enhanced input image to extract critical dehazing knowledge. Subsequently, in the MCP-KAF stage, the collected knowledge are further comprehensively analyzed and systematically fused through various complexity structure paths to get high-quality dehazed image. The effectiveness and generality of proposed framework have been experimentally verified on three publicly available datasets with diverse haze categories. All source code will be provided soon.