• Title/Summary/Keyword: 근사 DCT

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Image Reconstruction Using Poisson Model Screened from Image Gradient (이미지 기울기에서 선별된 포아송 모델을 이용한 이미지 재구성)

  • Kim, Yong-Gil
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.18 no.2
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    • pp.117-123
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    • 2018
  • In this study, we suggest a fast image reconstruction scheme using Poisson equation from image gradient domain. In this approach, using the Poisson equation, a guided vector field is created by employing source and target images within a selected region at the first step. Next, the guided vector is used in generating the result image. We analyze the problem of reconstructing a two-dimensional function that approximates a set of desired gradients and a data term. The joined data and gradients are able to work like modifying the image gradients while staying close to the original image. Starting with this formulation, we have a screened Poisson equation known in physics. This equation leads to an efficient solution to the problem in FFT domain. It represents the spatial filters that solve the two-dimensional screened Poisson model and shows gradient scaling to be a well-defined sharpen filter that generalizes Laplace sharpening. We demonstrate the results using a discrete cosine transformation based this Poisson model.

Block Histogram Compression Method for Selectivity Estimation in High-dimensions (고차원에서 선택율 추정을 위한 블록 히스토그램 압축방법)

  • Lee, Ju-Hong;Jeon, Seok-Ju;Park, Seon
    • The KIPS Transactions:PartD
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    • v.10D no.6
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    • pp.927-934
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    • 2003
  • Database query optimates the selectivety of a query to find the most efficient access plan. Multi-dimensional selectivity estimation technique is required for a query with multiple attributes because the attributes are not independent each other. Histogram is practically used in most commercial database products because it approximates data distributions with small overhead and small error rates. However, histogram is inadequate for a query with multiple attributes because it incurs high storage overhead and high error rates. In this paper, we propose a novel method for multi-dimentional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This enables low error rates and low storage overheads even in high dimensions. Extensive experimental results show adventages of the proposed approach.