I am writing regarding a paper on compressive sensing you may find of interest, co-authored with Xin Yuan, Jinli Suo, David Brady, and Qionghai Dai. We get exciting results on snapshot compressive imaging (SCI), i.e., encoding each frame of an image sequence with a spectral-, temporal-, or angular- variant random mask and summing them pixel-by-pixel to form one-shot measurement. Snapshot compressive hyperspectral, high-speed, and ligh-field imaging are among representatives.
We combine rank minimization to exploit the nonlocal self-similarity of natural scenes, which is widely acknowledged in image/video processing and alternating minimization approach to solve this problem. Results of both simulation and real data from four different SCI systems, where measurement noise is dominant, demonstrate that our proposed algorithm leads to significant improvements (>4dB in PSNR) and more robustness to noise compared with current state-of-the-art algorithms.
Paper arXiv link: https://arxiv.org/abs/1807.07837.
Github repository link: https://github.com/liuyang12/DeSCI.
Here is an animated demo for visualization and comparison with the state-of-the-art algorithms, , i.e., GMM-TP (TIP'14), MMLE-GMM (TIP'15), MMLE-MFA (TIP'15), and GAP-TV (ICIP'16).
Thanks Yang !
Rank Minimization for Snapshot Compressive Imaging by Yang Liu, Xin Yuan, Jinli Suo, David J. Brady, Qionghai Dai
Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications.This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications.
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