Improving nonlinear up-scaling by adapting to the local edge orientation

Tegenbosch, J.A.P.; Hofman, Paul; Bosma, Marco

doi:10.1117/12.525714

Cited by 18 publications

(17 citation statements)

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“…These settings were empirically determined to achieve good results. Figures 5 and 6 show results of TEXAS compared with the original image, linear scaling, and scaling involving nonlinear edge enhancement [14]. 3 Both the non-linear method and TEXAS improve the edge sharpness compared to linear scaling.…”

Section: Resultsmentioning

confidence: 97%

Single image super-resolution using self-examples and texture synthesis

Damkat

2011

SIViP

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An algorithm for single image super-resolution based on example-based super-resolution and example-based texture synthesis is proposed. While many other techniques for single image super-resolution are mainly effective on edges, the proposed algorithm enhances both edges and texture detail. The algorithm does not use an additional example database as it uses self-examples to synthesize new detail and texture, assuming that images contain a sufficient amount of self-similarity. The texture synthesis component of the algorithm enables the re-synthesis of texture at the output resolution to achieve super-resolution. The algorithm aims to create plausible, visually pleasing detail rather than reconstructing the true high-resolution image. Experimental results for natural images confirm the algorithms ability to create visually pleasing results, but also indicate that its performance is highly content dependent. Future efforts will be aimed at improving the robustness of the method.

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Section: Resultsmentioning

confidence: 97%

Single image super-resolution using self-examples and texture synthesis

Damkat

2011

SIViP

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“…24,25 In LTI, the gradient of an edge is improved by modifying both sides symmetrically around the edge center. According to Ref.…”

Section: Clipped Quadratic Weighted Median Filtermentioning

confidence: 99%

Artifact-free superresolution algorithm with natural texture preservation

Tai

Kuo

Chen

2014

J. Electron. Imaging

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Superresolution (SR) algorithms have recently become a hot research topic. The main purpose of image upscaling is to obtain high-resolution images from low-resolution ones, and these upscaled images should look like they had been taken with a camera having a resolution the same as the upscaled images, and at least present natural textures. In general, some SR algorithms preserve clear edges but blur the textures, while others preserve detailed textures but cause some obvious artifacts along edges. The proposed SR algorithm presents the detailed textures and, meanwhile, refines the strong edges and avoids causing obvious artifacts. The goal is achieved by using orthogonal fractal as the preliminary upscaling method in conjunction with the proper postprocessing where directional enhancement is adopted. In fact, the postprocessing part in the proposed SR algorithm can effectively reduce most jagged artifacts caused by SR algorithms. In the simulation results, it is shown that the proposed SR algorithm performs well in both objective and subjective measurements. Moreover, most detailed textures are properly enhanced and most jagged artifacts caused by SR algorithms can also be effectively reduced. © 2014 SPIE and IS&T [

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“…By amplifying the high frequency part of the signal, the resulting picture has a better sharpness impression. Most peaking have a single fixed peaking frequency (for example 2.7MHz for NTSC, 3.5MHz for PAL), while modern implementations like PixelPlus [3] and AutoTV [4] have multiple peaking filters so that they can adapt to frequency components that are available in the signal.…”

Section: Introductionmentioning

confidence: 99%

Block-based content-adaptive sharpness enhancement

Shao

Znamenskiy

2009

2009 IEEE 13th International Symposium on Consumer Electronics

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The contents of a video/TV sequence vary greatly not only across frames but also within the same frame. The existing peaking system like AutoTV where the peaking is made adaptive to the video contents and noise levels at a frame level is not sufficient for the high-end TV picture quality. This paper proposed a block-based content adaptive sharpness enhancement scheme that is able to adapt the peaking to the frame contents at a block level. In the proposed scheme, a frame is divided into a number of n × n blocks. Using some simple content-analysis techniques, each block is classified into categories with different characteristics to Human Visual System (HVS), such as the bright/dark block, the fine-details block, the strong-edge block, the smooth-area block, the skin/face block, etc. Appropriate processing is then applied to each block according to its category. The experimental results show that the proposed block-based content-adaptive peaking scheme achieves a better picture quality than the contentional peaking scheme.

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Improving nonlinear up-scaling by adapting to the local edge orientation

Cited by 18 publications

References 0 publications

Single image super-resolution using self-examples and texture synthesis

Single image super-resolution using self-examples and texture synthesis

Artifact-free superresolution algorithm with natural texture preservation

Block-based content-adaptive sharpness enhancement

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