Underwater Image Enhancement with Cascaded Contrastive Learning

Underwater image enhancement (UIE) is a highly challenging task due to the complexity of underwater environment and the diversity of underwater image degradation. Due to the application of deep learning, current UIE methods have made significant progress. Most of the existing deep learning-based UIE methods follow a single-stage network which cannot effectively address the diverse degradations simultaneously. In this paper, we propose to address this issue by designing a two-stage deep learning framework and taking advantage of cascaded contrastive learning to guide the network training of each stage. The proposed method is called CCL-Net in short. Specifically, the proposed CCL-Net involves two cascaded stages, i.e., a color correction stage tailored to the color deviation issue and a haze removal stage tailored to improve the visibility and contrast of underwater images. To guarantee the underwater image can be progressively enhanced, we also apply contrastive loss as an additional constraint to guide the training of each stage. In the first stage, the raw underwater images are used as negative samples for building the first contrastive loss, ensuring the enhanced results of the first color correction stage are better than the original inputs. While in the second stage, the enhanced results rather than the raw underwater images of the first color correction stage are used as the negative samples for building the second contrastive loss, thus ensuring the final enhanced results of the second haze removal stage are better than the intermediate color corrected results. Extensive experiments on multiple benchmark datasets demonstrate that our CCL-Net can achieve superior performance compared to many state-of-the-art methods. The source code of CCL-Net will be released at https://github.com/lewis081/CCL-Net.

FDCE-Net: Underwater Image Enhancement with Embedding Frequency and Dual Color Encoder

Underwater images often suffer from various issues such as low brightness, color shift, blurred details, and noise due to light absorption and scattering caused by water and suspended particles. Previous underwater image enhancement (UIE) methods have primarily focused on spatial domain enhancement, neglecting the frequency domain information inherent in the images. However, the degradation factors of underwater images are closely intertwined in the spatial domain. Although certain methods focus on enhancing images in the frequency domain, they overlook the inherent relationship between the image degradation factors and the information present in the frequency domain. As a result, these methods frequently enhance certain attributes of the improved image while inadequately addressing or even exacerbating other attributes. Moreover, many existing methods heavily rely on prior knowledge to address color shift problems in underwater images, limiting their flexibility and robustness. In order to overcome these limitations, we propose the Embedding Frequency and Dual Color Encoder Network (FDCE-Net) in our paper. The FDCE-Net consists of two main structures: (1) Frequency Spatial Network (FS-Net) aims to achieve initial enhancement by utilizing our designed Frequency Spatial Residual Block (FSRB) to decouple image degradation factors in the frequency domain and enhance different attributes separately. (2) To tackle the color shift issue, we introduce the Dual-Color Encoder (DCE). The DCE establishes correlations between color and semantic representations through cross-attention and leverages multi-scale image features to guide the optimization of adaptive color query. The final enhanced images are generated by combining the outputs of FS-Net and DCE through a fusion network. These images exhibit rich details, clear textures, low noise and natural colors.

WaterHE-NeRF: Water-ray Tracing Neural Radiance Fields for Underwater Scene Reconstruction

Neural Radiance Field (NeRF) technology demonstrates immense potential in novel viewpoint synthesis tasks, due to its physics-based volumetric rendering process, which is particularly promising in underwater scenes. Addressing the limitations of existing underwater NeRF methods in handling light attenuation caused by the water medium and the lack of real Ground Truth (GT) supervision, this study proposes WaterHE-NeRF. We develop a new water-ray tracing field by Retinex theory that precisely encodes color, density, and illuminance attenuation in three-dimensional space. WaterHE-NeRF, through its illuminance attenuation mechanism, generates both degraded and clear multi-view images and optimizes image restoration by combining reconstruction loss with Wasserstein distance. Additionally, the use of histogram equalization (HE) as pseudo-GT enhances the network's accuracy in preserving original details and color distribution. Extensive experiments on real underwater datasets and synthetic datasets validate the effectiveness of WaterHE-NeRF. Our code will be made publicly available.

DGNet: Dynamic Gradient-guided Network with Noise Suppression for Underwater Image Enhancement

Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. To solve this issue, previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features, limiting the generalization and adaptability of the model. Previous methods use the reference gradient that is constructed from original images and synthetic ground-truth images. This may cause the network performance to be influenced by some low-quality training data. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space. This process improves image quality and avoids local optima. Moreover, we propose a Feature Restoration and Reconstruction module (FRR) based on a Channel Combination Inference (CCI) strategy and a Frequency Domain Smoothing module (FRS). These modules decouple other degradation features while reducing the impact of various types of noise on network performance. Experiments on multiple public datasets demonstrate the superiority of our method over existing state-of-the-art approaches, especially in achieving performance milestones: PSNR of 25.6dB and SSIM of 0.93 on the UIEB dataset. Its efficiency in terms of parameter size and inference time further attests to its broad practicality. The code will be made publicly available.

IA2U: A Transfer Plugin with Multi-Prior for In-Air Model to Underwater

In underwater environments, variations in suspended particle concentration and turbidity cause severe image degradation, posing significant challenges to image enhancement (IE) and object detection (OD) tasks. Currently, in-air image enhancement and detection methods have made notable progress, but their application in underwater conditions is limited due to the complexity and variability of these environments. Fine-tuning in-air models saves high overhead and has more optional reference work than building an underwater model from scratch. To address these issues, we design a transfer plugin with multiple priors for converting in-air models to underwater applications, named IA2U. IA2U enables efficient application in underwater scenarios, thereby improving performance in Underwater IE and OD. IA2U integrates three types of underwater priors: the water type prior that characterizes the degree of image degradation, such as color and visibility; the degradation prior, focusing on differences in details and textures; and the sample prior, considering the environmental conditions at the time of capture and the characteristics of the photographed object. Utilizing a Transformer-like structure, IA2U employs these priors as query conditions and a joint task loss function to achieve hierarchical enhancement of task-level underwater image features, therefore considering the requirements of two different tasks, IE and OD. Experimental results show that IA2U combined with an in-air model can achieve superior performance in underwater image enhancement and object detection tasks. The code will be made publicly available.

Synergistic Multiscale Detail Refinement via Intrinsic Supervision for Underwater Image Enhancement

Visual restoration of underwater scenes is crucial for visual tasks, and avoiding interference from underwater media has become a prominent concern. In this work, we present a synergistic multiscale detail refinement via intrinsic supervision (SMDR-IS) to recover underwater scene details. The low-degradation stage provides multiscale detail for original stage, which achieves synergistic multiscale detail refinement through feature propagation via the adaptive selective intrinsic supervised feature module (ASISF), which achieves synergistic multiscale detail refinement. ASISF is developed using intrinsic supervision to precisely control and guide feature transmission in the multi-degradation stages. ASISF improves the multiscale detail refinement while reducing interference from irrelevant scene information from the low-degradation stage. Additionally, within the multi-degradation encoder-decoder of SMDR-IS, we introduce a bifocal intrinsic-context attention module (BICA). This module is designed to effectively leverage multi-scale scene information found in images, using intrinsic supervision principles as its foundation. BICA facilitates the guidance of higher-resolution spaces by leveraging lower-resolution spaces, considering the significant dependency of underwater image restoration on spatial contextual relationships. During the training process, the network gains advantages from the integration of a multi-degradation loss function. This function serves as a constraint, enabling the network to effectively exploit information across various scales. When compared with state-of-the-art methods, SMDR-IS demonstrates its outstanding performance. Code will be made publicly available.

AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection

In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection. AMSP-UOD specifically addresses the impact of non-ideal imaging factors on detection accuracy in complex underwater environments. To mitigate the influence of noise on object detection performance, we propose AMSP Vortex Convolution (AMSP-VConv) to disrupt the noise distribution, enhance feature extraction capabilities, effectively reduce parameters, and improve network robustness. We design the Feature Association Decoupling Cross Stage Partial (FAD-CSP) module, which strengthens the association of long and short-range features, improving the network performance in complex underwater environments. Additionally, our sophisticated post-processing method, based on non-maximum suppression with aspect-ratio similarity thresholds, optimizes detection in dense scenes, such as waterweed and schools of fish, improving object detection accuracy. Extensive experiments on the URPC and RUOD datasets demonstrate that our method outperforms existing state-of-the-art methods in terms of accuracy and noise immunity. AMSP-UOD proposes an innovative solution with the potential for real-world applications. Code will be made publicly available.

SnowFormer: Scale-aware Transformer via Context Interaction for Single Image Desnowing

Single image desnowing is a common yet challenging task. The complex snow degradations and diverse degradation scales demand strong representation ability. In order for the desnowing network to see various snow degradations and model the context interaction of local details and global information, we propose a powerful architecture dubbed as SnowFormer. First, it performs Scale-aware Feature Aggregation in the encoder to capture rich snow information of various degradations. Second, in order to tackle with large-scale degradation, it uses a novel Context Interaction Transformer Block in the decoder, which conducts context interaction of local details and global information from previous scale-aware feature aggregation in global context interaction. And the introduction of local context interaction improves recovery of scene details. Third, we devise a Heterogeneous Feature Projection Head which progressively fuse features from both the encoder and decoder and project the refined feature into the clean image. Extensive experiments demonstrate that the proposed SnowFormer achieves significant improvements over other SOTA methods. Compared with SOTA single image desnowing method HDCW-Net, it boosts the PSNR metric by 9.2dB on the CSD testset. Moreover, it also achieves a 5.13dB increase in PSNR compared with general image restoration architecture NAFNet, which verifies the strong representation ability of our SnowFormer for snow removal task. The code is released in \url{https://github.com/Ephemeral182/SnowFormer}.