Structure-Inferred Bi-level Model for Underwater Image Enhancement

Mu, Pan; Qian, Haotian; Bai, Cong

doi:10.1145/3503161.3548087

Cited by 14 publications

(4 citation statements)

References 38 publications

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“…Fu et al [22] proposed a SCNet underwater image enhancement network based on spatial and channel dimension normalization. SIBM [23] incorporates knowledge of semantic, gradient, and pixel domains to hierarchically enhance underwater images. By considering underwater images in extreme scenarios, ReX-Net [7] leverages the complementary information of reflectance and utilizes attention mechanisms to enhance channel and spatial information.…”

Section: Underwater Image Enhancementmentioning

confidence: 99%

Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement

Qu,

Song,

Chen

2024

Applied Sciences

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In recent decades, enhancing underwater images has become a crucial challenge when obtaining high-quality visual information in underwater environment detection, attracting increasing attention. Original underwater images are affected by a variety of underwater environmental factors and exhibit complex degradation phenomena such as low contrast, blurred details, and color distortion. However, most encoder-decoder-based methods fail to restore the details of underwater images due to information loss during downsampling. The noise in images also influences the recovery of underwater images with complex degradation. In order to address these challenges, this paper introduces a simple but effective denoising multiscale back-projection feature fusion network, which represents a novel approach to restoring underwater images with complex degradation. The proposed method incorporates a multiscale back-projection feature fusion mechanism and a denoising block to restore underwater images. Furthermore, we designed a multiple degradation knowledge distillation strategy to extend our method to enhance various types of degraded images, such as snowy images and hazy images. Extensive experiments on the standard datasets demonstrate the superior performance of the proposed method. Qualitative and quantitative analyses validate the effectiveness of the model compared to several state-of-the-art models. The proposed method outperforms previous deep learning models in recovering both the blur and color bias of underwater images.

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Section: Underwater Image Enhancementmentioning

confidence: 99%

Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement

Qu,

Song,

Chen

2024

Applied Sciences

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“…Underwater image enhancement (UIE) (Wei, Zheng, and Jia 2022;Mu, Qian, and Bai 2022;) is an important research topic in the field of computer vision. Due to the absorption and attenuation of light, underwater images will suffer from color shift and detail distortion (Xu et al 2023; González-Sabbagh and Robles-Kelly 2023; Jiang et al 2022b).…”

Section: Introductionmentioning

confidence: 99%

Underwater Organism Color Fine-Tuning via Decomposition and Guidance

Cong,

Gui,

Hou

2024

AAAI

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Due to the wavelength dependent light attenuation and scattering, the color of the underwater organism usually appears distorted. The existing underwater image enhancement methods mainly focus on designing networks capable of generating enhanced underwater organisms with fixed color. Due to the complexity of the underwater environment, ground truth labels are difficult to obtain, which results in the non-existence of perfect enhancement effects. Different from the existing methods, this paper proposes an algorithm with color enhancement and color fine-tuning (CECF) capabilities. The color enhancement behavior of CECF is the same as that of existing methods, aiming to restore the color of the distorted underwater organism. Beyond this general purpose, the color fine-tuning behavior of CECF can adjust the color of organisms in a controlled manner, which can generate enhanced organisms with diverse colors. To achieve this purpose, four processes are used in CECF. A supervised enhancement process learns the mapping from a distorted image to an enhanced image by the decomposition of color code. A self reconstruction process and a cross-reconstruction process are used for content-invariant learning. A color fine-tuning process is designed based on the guidance for obtaining various enhanced results with different colors. Experimental results have proven the enhancement ability and color fine-tuning ability of the proposed CECF. The source code is provided in https://github.com/Xiaofeng-life/CECF.

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“…Besides, losses like MSE often encourage to attain an average of realistic color images that match the input image. Recently, transformer-based models have shown promising results in various vision tasks Mu et al (2022Mu et al ( , 2021; Xie et al (2021); Zhou et al (2022b) and image colorization Ji et al (2022); Weng et al (2022a); Kumar et al (2021), addressing some of the problems derived from CNNs and synthesizing vibrant colors while preserving semantic realism. For example, ColTran Kumar et al (2021) uses conditional auto-regressive transformer for colorization and CT 2 Weng et al (2022a) proposes color transformer that interacts with color tokens and constricts the chroma candidates.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Multiple-Style Image Colorization through Context-Aware Codebook and Multi-Stage Learning

Liu,

Xu,

2024

Preprint

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Single image colorization is an inherently ill-posed problem that has attracted more research interest recently. It challenges to learn a luminance-chroma mapping and manage multi-modal uncertainty. However, directly learning a luminance-chroma mapping often results in issues such as semantic mismatch and color bleeding while reconstructing the chroma channels. Furthermore, the problem of output diversity remains unaddressed. To this end, we propose CodeColorist, a novel multi-stage network that effectively tackles the colorization process. To enhance the reconstruction of the chroma channels and overcome issues like semantic mismatch and color bleeding, in the first stage, we focus on learning a sophisticated and context-aware codebook by leveraging the interplay between the features extracted from luminance and chroma channels. In the second stage, we reframe the colorization problem as a code prediction task and learn to predict the code sequence for accurate codebook lookup. In addition, we address the issue of stochastic diverse output in colorization by navigating through an expanded hidden representation space in the final stage. This allows us to produce a wide range of diverse and visually appealing colorized images. Thanks to our tailored feature extraction architecture, expressive codebook, and designed lookup scheme, we are able to achieve vibrant and realistic colorized images that outperform previous works. Source code will be released in https://github.com/OHaiYo-lzy/CodeColorist.

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Structure-Inferred Bi-level Model for Underwater Image Enhancement

Cited by 14 publications

References 38 publications

Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement

Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement

Underwater Organism Color Fine-Tuning via Decomposition and Guidance

Enhancing Multiple-Style Image Colorization through Context-Aware Codebook and Multi-Stage Learning

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