Abstract
The objective of image dehazing is to restore the clear content from a hazy image. However, different parts of the same image pose varying degrees of difficulty for recovery. Existing image dehazing networks treat channel and pixel features equally, making it challenging to handle images with non-uniform haze distribution and weighted channels. To address this limitation, we propose a feature attention module for all-in-one image dehazing. The feature attention module comprises channel attention and pixel attention, offering enhanced flexibility in processing different types of information. Specifically, we perform stitching between adjacent layers in the channel dimension during feature extraction. Subsequently, we apply channel attention and pixel attention on the stitching layer with a large channel dimension. To preserve detailed texture features and minimize information loss from the attention mechanism, we use summation operations between the feature layer obtained after each attention operation and the input layer. Our model prioritizes attention to the dense haze region while maintaining overall brightness. Extensive experiments demonstrate that our method surpasses state-of-the-art dehazing techniques in terms of performance, requiring fewer parameters and FLOPs.
Supported by Liaoning Education Department General Project Foundation (LJKZ0231, LJKZZ20220033); Huaian Natural Science Research Plan Project Foundation (HAB202083).
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Dai, Q., Cui, T., Zhang, M., Zhao, X., Hou, B. (2023). All-in-One Image Dehazing Based on Attention Mechanism. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14268. Springer, Singapore. https://doi.org/10.1007/978-981-99-6486-4_5
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