Infrared image dehazing based on hierarchical subdivision superpixels and information integrity prior

CAS Key Laboratory of Infrared System Detection and Imaging Technology

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Shanghai Key Laboratory of Criminal Scene Evidence funded Foundation

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    Hazy weather degrades the contrast and visual quality of infrared imaging systems due to the presence of suspended particles. Most existing dehazing methods focus on enhancing global image contrast or exploit a local grid window strategy, which may lead to loss of information, halo artifacts and distortion in sky region. To address these problems, this paper proposes a novel single image dehazing model based on superpixel structure decomposition and protection of information integrity. In this model, based on the local structure information, the image is first adaptively divided into multiple objective regions using a superpixel segmentation algorithm to eliminate halo artifacts. Meanwhile, to avoid the error estimate caused by the local highlighted targets, a modified quadtree subdivision based on superpixel blocks is applied to obtain the global atmospheric light. Furthermore, a combined constraint is used to optimize the transmission map by minimizing the loss of information. Compared with state-of-the-art methods in terms of qualitative and quantitative analysis, experiments on real-world IR image data demonstrate the efficacy of the proposed method in both contrast and visibility.

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  • Received:August 18,2021
  • Revised:October 27,2021
  • Adopted:November 11,2021
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