Abstract
We propose a method to acquire compressed measurements for efficient video reconstruction using a single-pixel camera. The method is suitable for implementation using a single-pixel detector, along with a digital micromirror device or other types of spatial light modulators. Conventional implementations of single-pixel cameras are able to spatially compress the signal, but the compressed measurements make it difficult to exploit temporal redundancies directly. Moreover, a single-pixel camera needs to make measurements in a sequential manner before the scene changes, making it inefficient for video imaging. We discuss a measurement scheme that exploits sparsity along the time axis for video imaging. After acquiring all measurements required for the first frame, measurements are acquired only from the areas that change in subsequent frames. We segment the first frame, detect the magnitude and direction of change for each segment, and acquire compressed measurements for the changing segments in the predicted direction. Next, we compare the reconstruction results for a few test sequences with existing techniques and demonstrate the practical utility of the scheme.
Highlights
The compressed sensing (CS) framework for image acquisition exploits the inherent properties of a signal to reduce the number of samples required for reconstruction
We have discussed in this article a new scheme to acquire measurements for video reconstruction
The motion estimation is very efficient from a hardware implementation perspective
Summary
The compressed sensing (CS) framework for image acquisition exploits the inherent properties of a signal to reduce the number of samples required for reconstruction. Motion estimation/motion compensation BCS (ME/MS-BCS) selects a group of pictures (GOP) for estimating motion vectors and reconstruct the GOP using this information This improves the subrate performance but incurs an undesirable time delay in addition to increasing reconstruction complexity.[7,8,9,10] One other approach is adaptive block-based compressive sensing in which a frame is divided into a specific number of blocks and each block is assigned measurements based on changes and texture.[11] This approach accumulates residual error and gives block artifacts after recovery. The most effective application of this scheme is for videos with slow changes over time or few spatially dynamic regions It reduces the computation required after each frame, gives comparable peak signal-to-noise ratio (PSNR) with other techniques with the least number of measurements, and is implementable on a single-pixel DMD camera
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