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DTSTAMP:20230103T035306Z
LOCATION:Auditorium\, Level 5\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T100000
DTEND;TZID=Asia/Seoul:20221206T120000
UID:siggraphasia_SIGGRAPH Asia 2022_sess153_papers_484@linklings.com
SUMMARY:Constant Time Median Filter using 2D Wavelet Matrix
DESCRIPTION:Technical Papers\n\nConstant Time Median Filter using 2D Wavel
 et Matrix\n\nMoroto, Umetani\n\nThe median filter is a simple yet powerful
  noise reduction technique that is extensively applied in image, signal, a
 nd speech processing. It can effectively remove impulsive noise while pres
 erving the content of the image by taking the median of neighboring pixels
 ; thus, it has various applications, such as restoration of a damaged imag
 e and facial beautification. The median filter is typically implemented in
  one of two major approaches: the histogram-based method, which requires O
 (1) computation time per pixel when focusing on the kernel radius r, and t
 he sorting-based method, which requires approximately O(r^2) computation t
 ime per pixel but has a light constant factor. These are used differently 
 depending on the kernel radius and the number of bits in the image. Howeve
 r, the computation time is still slow, particularly when the kernel radius
  is in the mid to large range. \n\nThis paper introduces novel and efficie
 nt median filter with constant complexity O(1) for kernel size using the w
 avelet matrix data structure, which has been applied to query-based search
 es on one-dimensional data. We extended the original wavelet matrix to two
 -dimensional data for application to computer graphics problems. The objec
 tive of this study was to achieve high-speed median filter computation in 
 parallel computing environment with many threads (i.e., GPUs). Our impleme
 ntation for the GPU is an order of magnitude faster than the histogram met
 hod for 8-bit images. Unlike traditional histogram methods, which suffer f
 rom significant computational overhead, the proposed method can handle ima
 ges with high pixel depth (e.g., 16- and 32-bit high dynamic range images)
 . When the kernel radius is greater than 12 for 8-bit images, the proposed
  method outperforms the other median filter computation methods.\n\nRegist
 ration Category: FULL ACCESS, EXPERIENCE PLUS ACCESS, EXPERIENCE ACCESS, T
 RADE EXHIBITOR\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_484&sess=sess15
 3
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