标题： 颜色校正融合跨光谱优化：一种用于水下图像恢复的分布感知扩散方法 显示英文标题

标题： Color Correction Meets Cross-Spectral Refinement: A Distribution-Aware Diffusion for Underwater Image Restoration

摘要： 水下成像经常受到显著的视觉退化影响，这限制了其在后续应用中的适用性。 虽然最近的水下图像增强（UIE）方法依赖于深度神经网络架构设计的最新进展，但在跨场景鲁棒性和计算效率方面仍有很大的改进空间。 扩散模型在图像生成方面表现出色，促使我们考虑将其应用于UIE任务。 然而，直接将它们应用于UIE任务将带来两个挑战，\textit{即}，即高计算预算和颜色不平衡的扰动。 为了解决这些问题，我们提出了DiffColor，一种分布感知的扩散和跨谱细化模型，用于高效的UIE。 我们不是在原始像素空间中进行扩散，而是将图像转换到小波域以获得低频和高频谱，这在每次变换后固有地将图像空间维度减半。 与单噪声图像恢复任务不同，由于水对光的选择性吸收，水下成像表现出不平衡的通道分布。 为了解决这个问题，我们设计了全局颜色校正（GCC）模块来处理多样的颜色偏移，从而在去噪过程中避免潜在的全局退化干扰。 对于由水下散射导致的牺牲图像细节，我们进一步提出了跨谱细节细化（CSDR）来增强高频细节，这些细节与低频信号一起作为引导扩散的输入条件。 这种方式不仅确保了采样内容的高保真度，还补偿了被牺牲的细节。 全面的实验表明，DiffColor在定量和定性评估方面均优于最先进的方法。 显示英文摘要

摘要： Underwater imaging often suffers from significant visual degradation, which limits its suitability for subsequent applications. While recent underwater image enhancement (UIE) methods rely on the current advances in deep neural network architecture designs, there is still considerable room for improvement in terms of cross-scene robustness and computational efficiency. Diffusion models have shown great success in image generation, prompting us to consider their application to UIE tasks. However, directly applying them to UIE tasks will pose two challenges, \textit{i.e.}, high computational budget and color unbalanced perturbations. To tackle these issues, we propose DiffColor, a distribution-aware diffusion and cross-spectral refinement model for efficient UIE. Instead of diffusing in the raw pixel space, we transfer the image into the wavelet domain to obtain such low-frequency and high-frequency spectra, it inherently reduces the image spatial dimensions by half after each transformation. Unlike single-noise image restoration tasks, underwater imaging exhibits unbalanced channel distributions due to the selective absorption of light by water. To address this, we design the Global Color Correction (GCC) module to handle the diverse color shifts, thereby avoiding potential global degradation disturbances during the denoising process. For the sacrificed image details caused by underwater scattering, we further present the Cross-Spectral Detail Refinement (CSDR) to enhance the high-frequency details, which are integrated with the low-frequency signal as input conditions for guiding the diffusion. This way not only ensures the high-fidelity of sampled content but also compensates for the sacrificed details. Comprehensive experiments demonstrate the superior performance of DiffColor over state-of-the-art methods in both quantitative and qualitative evaluations.