Edge-Guided Image Gap Interpolation Using Multi-Scale Transformation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F16%3A43901643" target="_blank" >RIV/60461373:22340/16:43901643 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/16:00306283
Result on the web
<a href="http://ieeexplore.ieee.org/document/7511757/" target="_blank" >http://ieeexplore.ieee.org/document/7511757/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TIP.2016.2590825" target="_blank" >10.1109/TIP.2016.2590825</a>

Result language
angličtina
Original language name
Edge-Guided Image Gap Interpolation Using Multi-Scale Transformation
Original language description
This paper presents improvements in image gap restoration through the incorporation of edge-based directional interpolation within multi-scale pyramid transforms. Two types of image edges are reconstructed: 1) the local edges or textures, inferred from the gradients of the neighboring pixels and 2) the global edges between image objects or segments, inferred using a Canny detector. Through a process of pyramid transformation and downsampling, the image is progressively transformed into a series of reduced size layers until at the pyramid apex the gap size is one sample. At each layer, an edge skeleton image is extracted for edge-guided interpolation. The process is then reversed; from the apex, at each layer, the missing samples are estimated (an iterative method is used in the last stage of upsampling), up-sampled, and combined with the available samples of the next layer. Discrete cosine transform and a family of discrete wavelet transforms are utilized as alternatives for pyramid construction. Evaluations over a range of images, in regular and random loss pattern, at loss rates of up to 40%, demonstrate that the proposed method improves peak-signal-to-noise-ratio by 1-5 dB compared with a range of best published works.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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