GAP-net for Snapshot Compressive Imaging
Ziyi Meng, Shirin Jalali, Xin Yuan

TL;DR
GAP-net is a novel deep unfolding algorithm for snapshot compressive imaging that combines the GAP algorithm with CNN-based denoisers, offering high accuracy, speed, and flexibility for high-dimensional image reconstruction.
Contribution
This paper introduces GAP-net, a deep unfolding method for SCI that integrates trained CNN denoisers into the GAP algorithm, with proven convergence and versatile application.
Findings
GAP-net achieves competitive accuracy on synthetic and real data.
GAP-net demonstrates high speed and flexibility across different SCI systems.
Theoretical proof of global convergence for GAP-net with auto-encoder denoisers.
Abstract
Snapshot compressive imaging (SCI) systems aim to capture high-dimensional (D) images in a single shot using 2D detectors. SCI devices include two main parts: a hardware encoder and a software decoder. The hardware encoder typically consists of an (optical) imaging system designed to capture {compressed measurements}. The software decoder on the other hand refers to a reconstruction algorithm that retrieves the desired high-dimensional signal from those measurements. In this paper, using deep unfolding ideas, we propose an SCI recovery algorithm, namely GAP-net, which unfolds the generalized alternating projection (GAP) algorithm. At each stage, GAP-net passes its current estimate of the desired signal through a trained convolutional neural network (CNN). The CNN operates as a denoiser that projects the estimate back to the desired signal space. For the GAP-net that employs…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSparse and Compressive Sensing Techniques · Image and Signal Denoising Methods · Photoacoustic and Ultrasonic Imaging
