Underwater images are actively utilized for ocean observation and various applications related to marine science. They, however, suffer from image quality degradation due to underwater imaging which impedes in-depth image analysis. While deep models are effectively applied to correct degraded images, underwater image enhancement (UIE) specifically poses two issues regarding severe color distortion and scarce training data. To cope with those two issues, we propose a novel framework of multi-channel enhanced Glow, dubbed as MC-Glow, which effectively harnesses a deep model to enhance underwater images. Our deep model built upon invertible Glow structure is equipped with a multi-channel processing for resolving severe color degradation. In contrast to standard processing of full colors, the multi-channel branch encodes RGB colors separately to suppress interference among distorted color channels. Besides, we explore the possibility of leveraging the white balance dataset for pre-training the deep model. The pre-training dataset is composed of easy-to-access photos unlike underwater images and endows the model with effective image enhancement by mitigating scarcity of underwater images. Experimental results on UIE tasks using two benchmark datasets demonstrate that the proposed MC-Glow produces competitive performance with the other UIE approaches. Code is available at https://github.com/tkswalk/2025_Signal-Processing/tree/main . • We propose MC-Glow model to enhance underwater images of heavy color degradation. • Two-branch modules in MC-Glow mitigate channel-imbalanced color attenuation. • Large scale white-balance dataset is effectively leveraged to pretrain the model. • The method produces favorable performance on several underwater image datasets.
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