Invention Grant
- Patent Title: Image upsampling by learning pairs of low-resolution dictionaries using a structured subspace model
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Application No.: US16779121Application Date: 2020-01-31
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Publication No.: US11275967B2Publication Date: 2022-03-15
- Inventor: Faisal M. al-Salem
- Applicant: CICADA Imaging Inc.
- Applicant Address: SA Riyadh
- Assignee: CICADA Imaging Inc.
- Current Assignee: CICADA Imaging Inc.
- Current Assignee Address: SA Riyadh
- Agency: Berenato & White, LLC
- Main IPC: G06K9/32
- IPC: G06K9/32 ; G06K9/36 ; G06K9/62 ; G06T3/40

Abstract:
A computational method is disclosed for producing a sequence of high-resolution (HR) images from an input sequence of low-resolution (LR) images. The method uses a structured subspace framework to learn pairs of LR dictionaries from the input LR sequence ‘and’ employ learned pairs of LR dictionaries into estimating HR images. The structured subspace framework itself is based on a pair of specially structured HR basis matrices, wherein a HR basis spans any HR image whose so-called polyphase components (PPCs) are spanned by the corresponding LR dictionary.
Public/Granted literature
- US20210019561A1 IMAGE UPSAMPLING BY LEARNING PAIRS OF LOW-RESOLUTION DICTIONARIES USING A STRUCTURED SUBSPACE MODEL Public/Granted day:2021-01-21
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