论文标题
原始图像的多尺度视频Denoising算法
A Multi-scale Video Denoising Algorithm for Raw Image
论文作者
论文摘要
原始图像的视频降级一直是相机图像处理的困难。一方面，图像降级性能在很大程度上确定了图像质量，此外，在原始图像中的降解效果将影响ISP处理流的以下操作的准确性。另一方面，与图像相比，视频具有时间顺序的运动信息，因此在视频降低时需要复杂且计算昂贵的运动估计。鉴于上述问题，本文提出了一种针对原始图像的视频Deno算法，基于卷积神经网络在RAW-RGB图像上执行多个级联处理阶段，并在网络中执行隐式运动估计。具有最小的计算和带宽的传统算法的脱牙性能要优越，并且与大多数深度学习算法相比具有计算优势。
Video denoising for raw image has always been the difficulty of camera image processing. On the one hand, image denoising performance largely determines the image quality, moreover denoising effect in raw image will affect the accuracy of the following operations of ISP processing flow. On the other hand, compared with image, video have motion information in time sequence, thus motion estimation which is complex and computationally expensive is needed in video denoising. In view of the above problems, this paper proposes a video denoising algorithm for raw image, performing multiple cascading processing stages on raw-RGB image based on convolutional neural network, and carries out implicit motion estimation in the network. The denoising performance is far superior to that of traditional algorithms with minimal computation and bandwidth, and has computational advantages compared with most deep learning algorithms.
