Single image super-resolution reconstruction in presence of mixed Poisson-Gaussian noise

Bajić, Buda; Lindblad, Joakim; Sladoje, Nataša

doi:10.1109/ipta.2016.7820962

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…With the limitation of the image capture hardware, image enhancement software is preferred (Komatsu et al, 1993). Image enhancement software technique can be divided into three categories such as image enhancement based on multi-frame reconstruction (Hou and Andrews, 1978;Li and Orchard, 2001;Farsiu et al, 2004;Ahmad and Qureshi, 2012;Faramarzi et al, 2013), single image interpolation technique (Kim et al, 1990;Kim and Su, 1993;Grover and Kasana, 2015;Yan et al, 2015;Bajić et al, 2016;Gohshi, 2016;Ngocho and Mwangi, 2016), and example based learning (Freeman et al, 2002;Yang et al, 2010;Tai et al, 2010;Kim and Kwon, 2008;Lu et al, 2011;Suo et al, 2017;Shi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…This technique can improve the quality in HF area. Bajić et al (2016) estimated blur area and noise in LR image to reconstruction HR image based on Poisson Gaussian distribution. In frequency domain, Gohshi (2016) analyzed single image super resolution using two dimensional fast Fourier transform with nonlinear processing for generating HF characteristic element in reconstruction process.…”

Section: Introductionmentioning

confidence: 99%

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Image enhancement based on neuro-fuzzy gradient profile clustering

Ngernplubpla¹,

Chitsobhuk²

2018

Sci. Res. Essays

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This paper proposes a technique for image enhancement using Neuro-fuzzy based gradient profile generation to reconstruct the high resolution image from a single low resolution one. The natural gradient priors are collected and their statistics are analyzed and learned through Neuro-fuzzy model. The model adopts powerful data adaptation from neural network and combines with fuzzy system to enhance the ability in knowledge interpretation and explanation in terms of natural language. The triplet gradient profile is then generated as a result. The gradient profile results are used to regulate the Gaussian weighted sum filter in enhancement process. Then, all the weights were appropriately adapted according to gradient priors. From the experimental results, it can be seen that the proposed algorithm can greatly compensate the contrast and noise distortion in the low resolution image and demonstrate successful recovery of the high resolution image with quantitatively and perceptually performance improvement.

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