AI 中文总结
PIC-UIE提出轻量级预测器-执行器框架，从RGB缩略图预测图像自适应校正并在YCbCr空间应用，以9,486参数实现高效水下图像增强，达到24.137 dB PSNR和55 FPS 4K处理。
AI 中文摘要
水下图像增强（UIE）旨在从因波长相关衰减和背散射而退化的图像中恢复可见性、色彩保真度和结构细节。最先进的UIE方法通常依赖大型骨干网络和密集的图像到图像预测，限制了其在边缘部署中的实用性。此外，完全在单一颜色空间中操作会将退化估计与亮度和色度校正耦合在一起。为解决这些挑战，我们提出了PIC-UIE，一种轻量级的预测器-执行器框架，从固定的$256\ imes256$ RGB缩略图预测图像自适应校正，并将其应用于YCbCr颜色空间中的原生分辨率输入。预测器产生七个输出，组织为空间校正、非线性亮度与耦合色度映射以及图像级颜色校准。深度图在训练期间对传输代理进行正则化，而推理仅使用RGB输入。PIC-UIE在$256\ imes256$下具有9,486个参数和0.094 GFLOPs，在UIEB-90上达到24.137 dB PSNR和0.9216 SSIM，在零样本LSUI上达到21.320 dB PSNR。在比较协议下，它进一步以55.0 FPS处理原生4K图像。这些结果表明，结构化校正预测为水下图像增强提供了密集RGB重建的有效且实用的替代方案。
英文摘要
Underwater image enhancement (UIE) aims to restore visibility, color fidelity, and structural detail from images degraded by wavelength-dependent attenuation and backscatter. State-of-the-art UIE methods often rely on large backbones and dense image-to-image prediction, limiting their practicality for edge deployment. Moreover, operating entirely in a single color space couples degradation estimation with luminance and chroma correction. To address these challenges, we propose PIC-UIE, a lightweight predictor--executor framework that predicts image-adaptive corrections from a fixed $256\times256$ RGB thumbnail and applies them to the native-resolution input in the YCbCr color space. The predictor produces seven outputs, organized into spatial correction, nonlinear luminance and coupled chroma mapping, and image-level color calibration. A depth map regularizes the transmission proxy during training, whereas inference uses only the RGB input. With 9,486 parameters and 0.094 GFLOPs at $256\times256$, PIC-UIE achieves 24.137 dB PSNR and 0.9216 SSIM on UIEB-90 and 21.320 dB PSNR on zero-shot LSUI. It further processes native 4K images at 55.0 FPS under the comparison protocol. These results show that structured correction prediction provides an effective and practical alternative to dense RGB reconstruction for underwater image enhancement.
