NDLI: Super-Interpolation With Edge-Orientation-Based Mapping Kernels for Low Complex <inline-formula> <tex-math notation=LaTeX>$2times $ </tex-math></inline-formula> Upscaling

Content Provider	IEEE Xplore Digital Library
Author	Jae-Seok Choi Munchurl Kim
Copyright Year	1992
Abstract	With the advent of ultrahigh-definition (UHD) video services, super-resolution (SR) techniques are often required to generate high-resolution (HR) images from low-resolution (LR) images, such as HD images. To generate such HR images and a video of UHD resolutions in limited computing devices with hardware and software, low complex but excellent SR methods are particularly required. In this paper, we present a novel and fast SR method, called super-interpolation (SI), by unifying an interpolation step and a quality-enhancement step. The proposed SI method utilizes edge-orientation (EO)-based pre-learned kernels, which inherits the simplicity of interpolation and the quality enhancement of SR. It performs SR directly from the initial resolution of an input image to the target resolution of an up-scaled output image without requiring any intermediate interpolated image. The proposed SI method involves offline training and online up-scaling phases. In the offline training phase, training LR image patches are clustered based on their edge orientations into different EO classes for which class-dependent linear mapping functions are learned between training LR and HR image patches. In up-scaling phase, an HR output image patch for each LR input image patch is generated by applying an appropriate linear mapping function selected based on the EO of LR input image patch. Our proposed SI method is intensively compared with the ten state-of-the-art SR methods for common image sets and many HD/UHD images. The experimental results show that the SI method yields the smallest running time and requires relatively small hardware resources. It outperforms the six state-of-the-art methods in average (peak signal-to-noise ratio) PSNR/(structural similarity) SSIM, and exhibits competitive or somewhat lower PSNR/SSIM performance compared with the others.
Sponsorship	IEEE Signal Processing Society
Starting Page	469
Ending Page	483
Page Count	15
File Size	5474777
File Format	PDF
ISSN	10577149
Volume Number	25
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2016-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Interpolation Silicon Dictionaries Image edge detection Image reconstruction Image resolution Training pre-learned kernel Super-resolution linear mappings regression edge-orientation based interpolation
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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