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Unified Convex Optimization Approach to Super-Resolution Based on\n Localized Kernels

2015/01/08 by Tamir Bendory, Shai Dekel, Bendory, Tamir +3
Computer Science · Engineering · Mathematics · #Advanced Image Processing Techniques #FOS: Computer and information sciences #Image and Signal Denoising Methods #Information Theory (cs.IT) #Numerical methods in inverse problems #Photoacoustic and Ultrasonic Imaging #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.48550/arxiv.1501.01825

openalex publication_date 2015/01/08 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28

Abstract

The problem of resolving the fine details of a signal from its coarse scale\nmeasurements or, as it is commonly referred to in the literature, the\nsuper-resolution problem arises naturally in engineering and physics in a\nvariety of settings. We suggest a unified convex optimization approach for\nsuper-resolution. The key is the construction of an interpolating polynomial\nbased on localized kernels. We also show that the localized kernels act as the\nconnecting thread to another wide-spread problem of stream of pulses.\n

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