Abstract: Devices, systems, and methods of blind deblurring and blind super-resolution utilizing internal patch recurrence. Small signal patches tend to repeat “as is” across multiple scales of a natural signal. This fractal-like behavior is utilized for signal processing tasks, including “Blind Deblurring” or “Blind Super-Resolution”, namely, removing signal blur or increasing signal resolution without a-priori knowledge of the underlying blur kernel. While the cross-scale patch recurrence is strong in signals taken under ideal conditions, the cross-scale patch recurrence significantly diminishes when the acquisition blur deviates from an ideal blur. These deviations from ideal patch recurrences are used for recovering the underlying (unknown) blur kernel. The correct blur kernel is recovered by seeking the kernel which maximizes the patch similarity across scales of a related “reference” signal.

Abstract: A method and system for providing liquid funds to a business, the method comprising the business issuing an accounts receivable (AR) invoice to a client, the AR invoice requesting payment of a total amount charged, the business registering with a funder for an automatically underwritten fund provision service, the business requesting liquid funds from the funder, the liquid funds having a value determined by the total amount charged on the AR invoice, and the business receiving requested liquid funds from the funder.

Abstract: Devices, systems, and methods of blind deblurring and blind super-resolution utilizing internal patch recurrence. Small signal patches tend to repeat “as is” across multiple scales of a natural signal. This fractal-like behavior is utilized for signal processing tasks, including “Blind Deblurring” or “Blind Super-Resolution”, namely, removing signal blur or increasing signal resolution without a-priori knowledge of the underlying blur kernel. While the cross-scale patch recurrence is strong in signals taken under ideal conditions, the cross-scale patch recurrence significantly diminishes when the acquisition blur deviates from an ideal blur. These deviations from ideal patch recurrences are used for recovering the underlying (unknown) blur kernel. The correct blur kernel is recovered by seeking the kernel which maximizes the patch similarity across scales of a related “reference” signal.

Abstract: The present invention is an iterative method for normalization of a probe image against the Eigenspace learned from a database of images. The invention is also an iterative method for normalizing the n images in a database, wherein the normalization is carried out without using a predetermined criterion.

Abstract: The present invention is an iterative method for normalization of a probe image against the Eigenspace learned from a database of images. The invention is also an iterative method for normalizing the n images in a database, wherein the normalization is carried out without using a predetermined criterion.