10.6084/M9.FIGSHARE.C.6117963.V1
Honghao Huang
Honghao
Huang
Jiajie Teng
Jiajie
Teng
Yu Liang
Yu
Liang
Chengyang Hu
Chengyang
Hu
Minghua Chen
Minghua
Chen
Sigang Yang
Sigang
Yang
Hongwei Chen
Hongwei
Chen
Key frames assisted hybrid encoding for photorealistic compressive video sensing
Snapshot compressive imaging (SCI) encodes high-speed scene video into a snapshot measurement and then computationally makes reconstructions, allowing for efficient high-dimensional data acquisition. Numerous algorithms, ranging from regularization-based optimization and deep learning, are being investigated to improve reconstruction quality, but they are still limited by the ill-posed and information-deficient nature of the standard SCI paradigm. To overcome these drawbacks, we propose a new key frames assisted hybrid encoding paradigm for compressive video sensing, termed KH-CVS, that alternatively captures short-exposure key frames without coding and long-exposure encoded compressive frames to jointly reconstruct photorealistic video. With the use of optical flow and spatial warping, a deep convolutional neural network framework is constructed to integrate the benefits of these two types of frames. Extensive experiments on both simulations and real data from the prototype we developed verify the superiority of the proposed method.
Artificial Intelligence and Image Processing
Optica Publishing Group
2022
2022-10-07
2022-10-07
Collection
10.6084/m9.figshare.c.6117963
CC BY 4.0