Proceedings Article10.1109/ICASSP.2012.6288124
Super-resolution by GMM based conversion using self-reduction image
Yuki Ogawa,Yasuo Ariki,Tetsuya Takiguchi +2 more
- 25 Mar 2012
- pp 1285-1288
TL;DR: This paper proposes a single-image, super-resolution approach using GMM (Gaussian Mixture Model)-based conversion, and confirmed the effectiveness of this proposed method through the experiments.
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Abstract: In recent years, super-resolution techniques in the field of computer vision have been studied actively owing to the potential applicability in various fields. In this paper, we propose a single-image, super-resolution approach using GMM (Gaussian Mixture Model)-based conversion. The conversion function is constructed by GMM using the input image and its self-reduction image. The high-resolution image is obtained by applying the conversion function to the enlarged input image without any outside database. We confirmed the effectiveness of this proposed method through the experiments.
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Citations
Super-resolution: a comprehensive survey
Kamal Nasrollahi,Thomas B. Moeslund +1 more
- 01 Aug 2014
TL;DR: The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy, and common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super- resolution algorithms, and the most commonly employed databases are discussed.
Medical Image Compression Approach Based on Image Resizing, Digital Watermarking and Lossless Compression
Hedi Amri,Ali Khalfallah,Malek Gargouri,Naima Nebhani,Jean-Christophe Lapayre,Mohamed-Salim Bouhlel +5 more
- 01 May 2017
TL;DR: This work has proposed new approaches, which combined image reduction and expansion techniques, digital watermarking and lossless compression standards such as JPEG-LS (JLS) and TIFF formats, and provided significant improvements over the well-known JPEG image compression standard.
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Semantic super-resolution: When and where is it useful?
TL;DR: Experimental results show that the semantic driven super-resolution can significantly improve over the original settings, and the benefits vs. the drawbacks of using semantic information are discussed.
37
High-Frequency Restoration Using Deep Belief Nets for Super-resolution
Toru Nakashika,Tetsuya Takiguchi,Yasuo Ariki +2 more
- 02 Dec 2013
TL;DR: This paper proposes a novel super-resolution method that is based on a statistical model but does not require any pairs of low and high-resolved images in the database, and uses Deep Belief Bets to restore high-frequency information from a low-resolution image.
Single Image Super-Resolution Based on Wasserstein GANs
Fei Wu,Bo Wang,Dagang Cui,Linhao Li +3 more
- 01 Jul 2018
TL;DR: A novel single image super-resolution method unifying deep residual network and Wasserstein generative adversarial nets is proposed aiming at generating a photo-realistic image with finer texture details.
6
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