• 제목/요약/키워드: Bergman distance

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INVARIANT METRICS AND COMPLETENESS

  • Pflug, Peter
    • 대한수학회지
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    • 제37권2호
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    • pp.269-284
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    • 2000
  • We discuss completeness with respect to the Caratheodory distance, the Kobayashi distance and the Beraman distance, respectively.

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ON DISTANCE ESTIMATES AND ATOMIC DECOMPOSITIONS IN SPACES OF ANALYTIC FUNCTIONS ON STRICTLY PSEUDOCONVEX DOMAINS

  • Arsenovic, Milos;Shamoyan, Romi F.
    • 대한수학회보
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    • 제52권1호
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    • pp.85-103
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    • 2015
  • We prove some sharp extremal distance results for functions in various spaces of analytic functions on bounded strictly pseudoconvex domains with smooth boundary. Also, we obtain atomic decompositions in multifunctional Bloch and weighted Bergman spaces of analytic functions on strictly pseudoconvex domains with smooth boundary, which extend known results in the classical case of a single function.

An Improvement Algorithm for the Image Compression Imaging

  • Hu, Kaiqun;Feng, Xin
    • Journal of Information Processing Systems
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    • 제16권1호
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    • pp.30-41
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    • 2020
  • Lines and textures are natural properties of the surface of natural objects, and their images can be sparsely represented in suitable frames such as wavelets, curvelets and wave atoms. Based on characteristics that the curvelets framework is good at expressing the line feature and wavesat is good at representing texture features, we propose a model for the weighted sparsity constraints of the two frames. Furtherly, a multi-step iterative fast algorithm for solving the model is also proposed based on the split Bergman method. By introducing auxiliary variables and the Bergman distance, the original problem is transformed into an iterative solution of two simple sub-problems, which greatly reduces the computational complexity. Experiments using standard images show that the split-based Bergman iterative algorithm in hybrid domain defeats the traditional Wavelets framework or curvelets framework both in terms of timeliness and recovery accuracy, which demonstrates the validity of the model and algorithm in this paper.