FDCE-Net: Underwater Image Enhancement with Frequency-Domain Decoupling and Semantic-Aware Color Encoding
المفاهيم الأساسية
The proposed FDCE-Net effectively enhances underwater images by decoupling degradation factors in the frequency domain and learning semantic-aware color representations, resulting in improved color fidelity, texture details, and overall visual quality compared to state-of-the-art methods.
الملخص
The paper presents the FDCE-Net, an end-to-end underwater image enhancement (UIE) network that consists of two main components:
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Frequency Spatial Network (FS-Net): This network aims to decouple the various degradation factors present in underwater images in the frequency domain. It employs a Frequency Spatial Residual Block (FSRB) to enhance different image attributes, such as brightness, color, texture, and noise, separately. This approach avoids the trade-off between different enhancement aspects that plagues many existing UIE methods.
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Dual Color Encoder (DCE): To address the color shift issue in underwater images, the DCE integrates CNN and Transformer architectures to establish correlations between color and semantic representations through cross-attention. It leverages multi-scale image features to guide the optimization of adaptive color queries, enabling improved color recovery without reliance on manually designed priors.
The final enhanced image is generated by combining the outputs of FS-Net and DCE through a fusion network. Extensive experiments on multiple benchmark datasets demonstrate that FDCE-Net outperforms state-of-the-art UIE methods in terms of both visual quality and quantitative metrics, such as SSIM, PSNR, UIQM, and UCIQE.
إعادة الكتابة بالذكاء الاصطناعي
إنشاء خريطة ذهنية
من محتوى المصدر
FDCE-Net: Underwater Image Enhancement with Embedding Frequency and Dual Color Encoder
الإحصائيات
Underwater images often suffer from low brightness, color shift, blurred details, and noise due to light absorption and scattering caused by water and suspended particles.
Previous UIE methods have primarily focused on spatial domain enhancement, neglecting the frequency domain information inherent in the images.
Many existing UIE methods heavily rely on prior knowledge to address color shift problems, limiting their flexibility and robustness.
اقتباسات
"The core challenge of existing UIE methods is the issue of uneven image enhancement, as it's difficult to avoid compromising one aspect for another, such as increasing brightness at the expense of introducing more noise, or achieving better color restoration while failing to maintain texture clarity."
"By decomposing images in the frequency domain, we decouple the factors contributing to image degradation, enabling separate enhancement of different aspects of the image. This is our major advantage compared to previous methods."
استفسارات أعمق
How can the proposed FDCE-Net be extended to handle other types of image degradation, such as haze or low-light conditions, in a unified framework
The FDCE-Net can be extended to handle other types of image degradation, such as haze or low-light conditions, by incorporating additional modules or components into the existing framework. For haze removal, a dehazing module can be integrated into the network architecture to address the scattering effects caused by particles in the atmosphere. This module can utilize techniques like dark channel prior or atmospheric scattering models to estimate and remove the haze from the images. Additionally, for low-light conditions, a low-light enhancement module can be included to adjust the exposure and brightness levels of the images to improve visibility in dark environments. By integrating these modules into the FDCE-Net, a unified framework can be created to handle a wider range of image degradation challenges in underwater environments.
What are the potential limitations of the Dual Color Encoder approach, and how could it be further improved to handle more complex underwater scenarios
The Dual Color Encoder approach, while effective in capturing the relationship between color and semantic representations, may have limitations in handling more complex underwater scenarios where color shifts are more varied and challenging. One potential limitation is the reliance on the initial color histogram for color enhancement, which may not always capture the diverse color variations present in underwater scenes. To address this limitation, the Dual Color Encoder can be further improved by incorporating adaptive color histogram generation techniques that dynamically adjust the color queries based on the specific color distribution in the input image. Additionally, introducing attention mechanisms that focus on specific color regions or objects in the image can enhance the color restoration process in complex underwater scenarios. By refining the color query generation and incorporating adaptive attention mechanisms, the Dual Color Encoder can be enhanced to handle a broader range of color variations and complexities in underwater images.
Given the importance of underwater imaging for various applications, how could the insights from this work be applied to enhance the performance of underwater vision tasks, such as object detection or semantic segmentation
The insights from this work on underwater image enhancement can be applied to enhance the performance of underwater vision tasks such as object detection or semantic segmentation by improving the quality and clarity of input images. For object detection, the enhanced images from the FDCE-Net can provide clearer visual cues and details, leading to improved object detection accuracy and localization. By enhancing the color fidelity and texture details in underwater images, the object detection models can better distinguish objects from the background and reduce false positives. Similarly, in semantic segmentation tasks, the enhanced images can provide more precise boundaries and textures for different semantic classes, resulting in more accurate segmentation results. By integrating the enhanced images as input to object detection and semantic segmentation models, the overall performance and reliability of underwater vision tasks can be significantly enhanced.
