Research On High Dynamic Range Scenes Imaging Algorithm Based On Deep Learning

In recent years,machine vision is widely used in fields such as autonomous driving and robot navigation.In these fields,vision algorithms often face high-dynamic-range(HDR)scenes,such as tree-lined roads and tunnels.How to image such HDR scenes is a valuable research topic.Aiming at above problems,we design a high-dynamic-range imaging(HDRI)algorithm based on deep learning,which mainly includes the following works:(1)An auto exposure algorithm that can minimize the number of bad pixel caused by severe poor exposure is proposed.In HDR scenes,the algorithm can automatically optimize the camera’s exposure time to minimize the number of bad pixel in the image.(2)A CNN called HDR-Net for camera image signal processing(ISP)and single-exposure HDRI is designed.HDR-Net is smaller than naive U-Net,and have a global feature block to deal with large textureless regions.HDR-Net performs demosaicing,denoising and enhancement on 12-bit Raw images and outputs an 8-bit RGB images,which can be directly used by subsequent image recognition algorithms.For training HDR-Net,we collect the Raw12 dataset,which contains 60 scenes,each scene has multiple 12-bit Raw images acquired in different exposure times and a reference image synthesized using multi-exposure HDRI technology.(3)The experimental platform is built.Our algorithm is tested on several typical scenarios and Raw12 datasets.Experiments show that the area of extremely poor exposure area in the image acquired by our automatic exposure algorithm is smaller than that acquired by the common algorithm.HDR-Net’s SSIM and PSNR on the Raw12 test set can reached 0.95 and 23.55 respectively.In the HDR scene,the overall algorithm constructed using the above two algorithm modules has high output image quality and is suitable for subsequent image recognition,and the running speed can reach 18 FPS when the input resolution is 2048 × 2448.