| 研究課題/領域番号 |
22K12101
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分61010:知覚情報処理関連
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| 研究機関 | 法政大学 |
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研究代表者 |
周 金佳 法政大学, 理工学部, 准教授 (50723392)
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| 研究分担者 |
谷口 一徹 大阪大学, 大学院情報科学研究科, 准教授 (40551453)
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| 研究期間 (年度) |
2022-04-01 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,160千円 (直接経費: 3,200千円、間接経費: 960千円)
2024年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2023年度: 1,560千円 (直接経費: 1,200千円、間接経費: 360千円)
2022年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
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| キーワード | Image sensing / Deep learning / Compressive sensing |
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| 研究開始時の研究の概要 |
This research proposes a new optical coding with AI based measurement coding and smart sparse recovery system that can greatly reduce the sensing power and compression power at the same time. It is the first time to design a sensing pattern that can efficiently compress the signal during sensing.
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| 研究実績の概要 |
The following tasks have been completed. (1) On the encoder side, building upon the adaptive sensing technique developed last year, we further optimized it and applied an edge detection-based and region-of-interest detection based adaptive sensing framework to enhance the quality of the reconstructed videos. (2) On the decoder side, we proposed an adaptive stage-activated unfolding network to adaptively control the complexity of reconstruction. (3) We optimized the entire system at the system level to realize a computer vision-oriented compressive sensing system. (4) To reduce the power consumption of the whole system, we proposed a framework for partial pre-calculation-based image encoding and decoding.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
As planned, the adaptive sampling algorithms based on key information extraction achieved excellent results in reducing computational complexity and improving the quality of reconstructed images/videos. Furthermore, the system-level optimization also yielded positive outcomes. We will continue to improve the performance of the whole system.
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| 今後の研究の推進方策 |
This project is divided into three main tasks, each building upon the progress made in the previous stages. The first task of developing measurement coding system at the encoder side has already been successfully completed. In the second task, we focused on extracting moving objects, edge information and region-of-interest as key information during the FY2022 and FY2023. Our plan in FY2024 is to refine and enhance the key information extraction algorithms, incorporating new techniques to effectively selecting the sampling ratio while maintaining video quality. Additionally, we aim to optimize the entire system for reducing the power consumption.
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