用AI流水线提升手机拍摄画质，兼顾速度与细节。
DRIFT: Deep Restoration, ISP Fusion, and Tone-mapping
- 分两阶段处理：多帧对齐去噪超分 + 可调色调映射
- 在真实手机上实现高清画面生成，性能优于现有方法
- 适合移动端图像增强研发者参考
智能手机摄像头因高分辨率和高动态范围成像的普及而广受欢迎。为此，高性能的移动图像信号处理器（ISP）在保证低计算成本的同时，对生成高质量图像至关重要。本文提出DRIFT（Deep Restoration, ISP Fusion, and Tone-mapping）：一种高效的AI手机相机流水线，可从手持原始图像生成高质量RGB图像。第一阶段为多帧处理（MFP）网络，采用对抗性感知损失训练，完成多帧对齐、去噪、去马赛克和超分辨率。随后，输出由新型深度学习色调映射（DRIFT-TM）模块处理，支持色调调节、保持与参考流水线的一致性，并可在移动设备上高效运行于高分辨率图像。通过定性和定量对比现有先进MFP与色调映射方法，验证了本方案的有效性。
原文摘要 · Abstract (English)
Smartphone cameras have gained immense popularity with the adoption of high-resolution and high-dynamic range imaging. As a result, high-performance camera Image Signal Processors (ISPs) are crucial in generating high-quality images for the end user while keeping computational costs low. In this paper, we propose DRIFT (Deep Restoration, ISP Fusion, and Tone-mapping): an efficient AI mobile camera pipeline that generates high quality RGB images from hand-held raw captures. The first stage of DRIFT is a Multi-Frame Processing (MFP) network that is trained using a adversarial perceptual loss to perform multi-frame alignment, denoising, demosaicing, and super-resolution. Then, the output of DRIFT-MFP is processed by a novel deep-learning based tone-mapping (DRIFT-TM) solution that allows for tone tunability, ensures tone-consistency with a reference pipeline, and can be run efficiently for high-resolution images on a mobile device. We show qualitative and quantitative comparisons against state-of-the-art MFP and tone-mapping methods to demonstrate the effectiveness of our approach.
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