Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing

Cheng Ying Tsai, Chieh Li Chen

研究成果: Article同行評審


In recent decades, haze has become an environmental issue due to its effects on human health. It also reduces visibility and degrades the performance of computer vision algorithms in autonomous driving applications, which may jeopardize car driving safety. Therefore, it is ex-tremely important to instantly remove the haze effect on an image. The purpose of this study is to leverage useful modules to achieve a lightweight and real-time image-dehazing model. Based on the U-Net architecture, this study integrates four modules, including an image pre-processing block, inception-like blocks, spatial pyramid pooling blocks, and attention gates. The original attention gate was revised to fit the field of image dehazing and consider different color spaces to retain the advantages of each color space. Furthermore, using an ablation study and a quantitative evaluation, the advantages of using these modules were illustrated. Through existing indoor and outdoor test datasets, the proposed method shows outstanding dehazing quality and an efficient execution time compared to other state-of-the-art methods. This study demonstrates that the proposed model can improve dehazing quality, keep the model lightweight, and obtain pleasing dehazing results. A comparison to existing methods using the RESIDE SOTS dataset revealed that the proposed model improves the SSIM and PSNR metrics by at least 5–10%.

期刊Applied Sciences (Switzerland)
出版狀態Published - 2022 7月 1

All Science Journal Classification (ASJC) codes

  • 材料科學(全部)
  • 儀器
  • 工程 (全部)
  • 製程化學與技術
  • 電腦科學應用
  • 流體流動和轉移過程


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