System and Method for Saliency-Adaptive Snapshot Compressive Imaging
AssigneeThe University of Hong Kong
InventorsYaping ZHAO, Edmund Yin Mun LAM
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Abstract
The present invention provides a self-adaptive and saliency-aware snapshot compressive imaging (SCI) system. In comparison with the existing SCI systems, the self-adaptive and saliency-aware SCI system of the present invention integrates saliency detection, which feedbacks to the coding masks with a calculated sampling efficiency for updating the coding masks. As such, self-adaptation of the system is achieved, and high-level information such as saliency is also obtained and given consideration, thereby producing reconstruction results with better quality, lower power cost and higher efficiency.
Claims12
A saliency-aware and self-adaptive snapshot compressive imaging system comprising:
- a modulating device including coding masks;
- a dynamic image detector;
- and
- a data processor;
- wherein the data processor perform image reconstruction and self-adaptive sampling, comprising:
- receiving a first compressed measurement signal obtained by projecting a dynamic scene to the modulating device and capturing by the dynamic image detector;
- subjecting the first compressed measurement signal to a reconstruction algorithm for image reconstruction and saliency detection to obtain saliency maps;
- calculating a sampling probability based on the saliency maps;
- and
- updating the coding masks of the modulating device with the sampling probability for the compression of a subsequent frame of the captured dynamic scene.
A saliency-aware and self-adaptive snapshot compressive imaging system comprising:
- a modulating device including coding masks;
- a dynamic image detector;
- and
- a data processor;
- wherein the data processor perform image reconstruction and self-adaptive sampling, comprising:
- receiving a first compressed measurement signal obtained by projecting a dynamic scene to the modulating device and capturing by the dynamic image detector;
- subjecting the first compressed measurement signal to a reconstruction algorithm for image reconstruction;
- subjecting the reconstructed image to saliency detection to obtain saliency maps;
- calculating a sampling probability based on the saliency maps;
- and
- updating the coding masks of the modulating device with the sampling probability for the compression of a subsequent frame of the captured dynamic scene.
Dependencies are inferred from the claim text.