期刊
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
卷 -, 期 -, 页码 2769-2773出版社
IEEE
DOI: 10.1109/ICASSP43922.2022.9747781
关键词
Underwater image enhancement; wavelet decomposition; multi-color space; dual-stream network
We propose a wavelet-based dual-stream network to address color cast and blurry details in underwater images. By decomposing the input image and using two sub-networks for color space fusion and detail enhancement, our model achieves effective color correction and blur removal.
We present a wavelet-based dual-stream network that addresses color cast and blurry details in underwater images. We handle these artifacts separately by decomposing an input image into multiple frequency bands using discrete wavelet transform, which generates the downsampled structure image and detail images. These sub-band images are used as input to our dual-stream network that incorporates two sub-networks: the multi-color space fusion network and the detail enhancement network. The multi-color space fusion network takes the decomposed structure image as input and estimates the color corrected output by employing the feature representations from diverse color spaces of the input. The detail enhancement network addresses the blurriness of the original underwater image by improving the image details from high-frequency sub-bands. We validate the proposed method on both real-world and synthetic underwater datasets and show the effectiveness of our model in color correction and blur removal with low computational complexity.
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