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KAKEN_25420383seika.pdf
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自己相似性を用いた画像の超解像に関する研究
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| Kana |
ジコ ソウジセイ オ モチイタ ガゾウ ノ チョウカイゾウ ニ カンスル ケンキュウ
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Jiko sojisei o mochiita gazo no chokaizo ni kansuru kenkyu
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Image super-resolution using similarity
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池原, 雅章
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イケハラ, マサアキ
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Ikehara, Masaaki
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慶應義塾大学・理工学部・教授
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Research team head
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科研費研究者番号 : 00212796
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2016
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科学研究費補助金研究成果報告書
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2015
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本研究課題では, 単一の画像から高速かつ高性能な超解像を行う幾つかの手法を提案する。一つは画像の自己相似性を利用し, 辞書学習を必要とせず, 自身の低解像度画像と原画像からランダムサンプルして辞書を作成し, これに基づいてL2近似により超解像を行う。もう一つはさらなる高速化を目指して, 低解像度画像の画素差のバイラテラル重みを補間係数として, 直接補間により高解像度化する超解像手法を提案する。これらの手法は従来法に比べ高速かつ高精度な超解像が実現できることを確認した。
In this research, we propose some fast and high performance super resolution methods from a single image. One method is based on the similarity of image. This makes a dictionary from random sampled patches from original and down-sampled images and apple L2 approximation to obtain a high resolution image. Another method is based on interpolation for high speed implementation. This utilize the bilateral weight of pixels in low resolution image as interpolation coefficient. With local interpolation coefficients, the high resolution images are obtained by applying direct interpolation of LR image. Through the simulations, the proposed methods are shown to be efficient for fast and high performance super resolution.
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研究種目 : 基盤研究(C)(一般)
研究期間 : 2013～2015
課題番号 : 25420383
研究分野 : 信号処理, 画像処理
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