Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration (2311.16845v1)

Abstract: Underwater images are subject to intricate and diverse degradation, inevitably affecting the effectiveness of underwater visual tasks. However, most approaches primarily operate in the raw pixel space of images, which limits the exploration of the frequency characteristics of underwater images, leading to an inadequate utilization of deep models' representational capabilities in producing high-quality images. In this paper, we introduce a novel Underwater Image Enhancement (UIE) framework, named WF-Diff, designed to fully leverage the characteristics of frequency domain information and diffusion models. WF-Diff consists of two detachable networks: Wavelet-based Fourier information interaction network (WFI2-net) and Frequency Residual Diffusion Adjustment Module (FRDAM). With our full exploration of the frequency domain information, WFI2-net aims to achieve preliminary enhancement of frequency information in the wavelet space. Our proposed FRDAM can further refine the high- and low-frequency information of the initial enhanced images, which can be viewed as a plug-and-play universal module to adjust the detail of the underwater images. With the above techniques, our algorithm can show SOTA performance on real-world underwater image datasets, and achieves competitive performance in visual quality.

• The paper introduces WF-Diff, a novel framework combining wavelet-based Fourier information interaction with diffusion adjustment for enhanced underwater image restoration.
• WF-Diff utilizes a Wavelet-based Fourier Information Interaction Network (WFI2-net) for frequency refinement and a Frequency Residual Diffusion Adjustment Module (FRDAM) for detailed correction.
• Quantitative analysis shows WF-Diff improves image quality metrics like PSNR and SSIM on real-world datasets, demonstrating superior performance over state-of-the-art methods.

Wavelet-based Fourier Information Interaction for Underwater Image Restoration

The paper "Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration" presents a novel framework, WF-Diff, to tackle the challenges associated with underwater image enhancement (UIE). This research focuses on addressing the limitations of traditional UIE methods, which predominantly process images in the raw pixel space, failing to fully capitalize on the frequency characteristics inherent in underwater imagery. The authors propose a sophisticated approach that leverages the intricate properties of both wavelet-based Fourier transforms and diffusion models.

Key Components of WF-Diff

The proposed framework comprises two distinct yet complementary networks:

1. Wavelet-based Fourier Information Interaction Network (WFI2-net): This component is designed to refine frequency domain information early in the enhancement process. Utilizing discrete wavelet transformations (DWT), the network separates input images into low-frequency and high-frequency sub-images. The high-frequency sub-images, which contain texture and detail degradation information, are enhanced using a Wide Transformer Block (WTB) capable of modeling long-range dependencies. Conversely, the low-frequency component, containing color degradation information, is refined using a Spatial-Frequency Fusion Block (SFFB), which harmonizes spatial and frequency domain data.
2. Frequency Residual Diffusion Adjustment Module (FRDAM): This module employs diffusion models to further adjust high- and low-frequency information, acting as a plug-and-play component for refining details. FRDAM differs from standard diffusion models by learning the residual distribution between preliminary enhancements and ground-truth images, thus mitigating potential artifacts arising from Gaussian noise samples and enhancing focus on fine-grained image details.

Quantitative and Qualitative Outcomes

The application of WF-Diff on real-world underwater datasets demonstrates considerable improvements in image quality metrics, such as PSNR and SSIM, along with decreased LPIPS and FID scores. Moreover, the framework exhibits superior performance compared to several state-of-the-art UIE methodologies, as evidenced by robust qualitative results on datasets like UIEBD, LSUI, and U45. These outcomes underscore the capability of WF-Diff to deliver visually compelling and high-quality underwater images devoid of traditional artifacts and distortions.

Implications and Future Directions

The insights extrapolated from frequency domain interactions and their integration with diffusion models mark a significant advancement in underwater image restoration techniques. Practically, the WF-Diff framework holds promise for diverse applications in underwater robotics, object tracking, and environmental monitoring, where pristine visual information is critical. Theoretically, it introduces innovative approaches to incorporating frequency domain information into AI models, paving the way for further explorations in leveraging frequency characteristics across other computer vision tasks beyond underwater image restoration.

Future investigations may focus on optimizing the inference speed of the diffusion models used in WF-Diff. Furthermore, additional research could explore extending the framework's principles to other challenging imaging environments, enhancing its adaptability and efficiency.

In sum, the paper offers valuable methodologies that enrich UIE technology through advanced image processing techniques, potentially catalyzing further developments in image quality enhancement across varied vision applications.