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Guided Linear Upsampling
Song, Shuangbing ; Zhong, Fan ; Wang, Tianju ; Qin, Xueying ; Tu, Changhe
ACM transactions on graphics, 2023-08, Vol.42 (4), p.1-12, Article 100
[Periódico revisado por pares]
New York, NY, USA: ACM
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Título:
Guided Linear Upsampling
Autor:
Song, Shuangbing
;
Zhong, Fan
;
Wang, Tianju
;
Qin, Xueying
;
Tu, Changhe
Assuntos:
Computing methodologies
;
Computing methodologies / Artificial intelligence
;
Computing methodologies / Artificial intelligence / Computer vision
;
Computing methodologies / Computer graphics
;
Computing methodologies / Computer graphics / Image manipulation
;
Computing methodologies / Computer graphics / Rendering
;
Mathematics of computing
;
Mathematics of computing / Mathematical analysis
;
Mathematics of computing / Mathematical analysis / Numerical analysis
;
Mathematics of computing / Mathematical analysis / Numerical analysis / Interpolation
É parte de:
ACM transactions on graphics, 2023-08, Vol.42 (4), p.1-12, Article 100
Descrição:
Guided upsampling is an effective approach for accelerating high-resolution image processing. In this paper, we propose a simple yet effective guided upsampling method. Each pixel in the high-resolution image is represented as a linear interpolation of two low-resolution pixels, whose indices and weights are optimized to minimize the upsampling error. The downsampling can be jointly optimized in order to prevent missing small isolated regions. Our method can be derived from the color line model and local color transformations. Compared to previous methods, our method can better preserve detail effects while suppressing artifacts such as bleeding and blurring. It is efficient, easy to implement, and free of sensitive parameters. We evaluate the proposed method with a wide range of image operators, and show its advantages through quantitative and qualitative analysis. We demonstrate the advantages of our method for both interactive image editing and real-time high-resolution video processing. In particular, for interactive editing, the joint optimization can be precomputed, thus allowing for instant feedback without hardware acceleration.
Editor:
New York, NY, USA: ACM
Idioma:
Inglês
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