用频域信噪比增强水下图像，恢复色彩与细节
SFormer: SNR-guided Transformer for Underwater Image Enhancement from the Frequency Domain
- 在频域利用信噪比引导特征分离，提升通道调节精度
- 相比现有方法，PSNR提升3.1 dB，SSIM提高0.08
- 适合水下影像修复、海洋视觉系统等应用
近期基于学习的水下图像增强方法通过将物理先验融入深度网络取得进展，尤其是利用信噪比（SNR）先验减少波长相关的衰减。然而，空间域的SNR先验存在两个局限：(i) 无法有效分离跨通道干扰，(ii) 在增强信息结构的同时抑制噪声方面帮助有限。为此，我们提出在频域使用SNR先验，将特征分解为幅值和相位谱以实现更好的通道调制。我们引入傅里叶注意力SNR先验变换器（FAST），结合谱间交互与SNR线索，突出关键谱成分。此外，频率自适应变换器（FAT）瓶颈通过门控注意力机制融合高低频分支，提升感知质量。嵌入统一的U型架构中，这些模块整合传统RGB流与SNR引导分支，构成SFFormer。在4,800对来自UIEB、EUVP和LSUI的数据上训练，SFFormer在PSNR上相较近期方法提升3.1 dB，SSIM提升0.08，成功恢复水下场景的色彩、纹理与对比度。
原文摘要 · Abstract (English)
Recent learning-based underwater image enhancement (UIE) methods have advanced by incorporating physical priors into deep neural networks, particularly using the signal-to-noise ratio (SNR) prior to reduce wavelength-dependent attenuation. However, spatial domain SNR priors have two limitations: (i) they cannot effectively separate cross-channel interference, and (ii) they provide limited help in amplifying informative structures while suppressing noise. To overcome these, we propose using the SNR prior in the frequency domain, decomposing features into amplitude and phase spectra for better channel modulation. We introduce the Fourier Attention SNR-prior Transformer (FAST), combining spectral interactions with SNR cues to highlight key spectral components. Additionally, the Frequency Adaptive Transformer (FAT) bottleneck merges low- and high-frequency branches using a gated attention mechanism to enhance perceptual quality. Embedded in a unified U-shaped architecture, these modules integrate a conventional RGB stream with an SNR-guided branch, forming SFormer. Trained on 4,800 paired images from UIEB, EUVP, and LSUI, SFormer surpasses recent methods with a 3.1 dB gain in PSNR and 0.08 in SSIM, successfully restoring colors, textures, and contrast in underwater scenes.
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