Image super resolution by fast edge-adaptive interpolation

Grover, Richa; Kasana, Singara Singh

doi:10.1109/icaccs.2015.7324075

Cited by 3 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…Alternative technique is to emphasize or to boost edges in HF area while smoothing the LF area to obtain the improvement of the HR image. In spatial domain, Grover and Kasana (2015) presented Fast Edge-Adaptive Interpolation technique based on approximated nonlinear parameters. This technique attempted to find edge area in difference resolution and group up edge area to connect and sharpen HF area.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Image enhancement based on neuro-fuzzy gradient profile clustering

Ngernplubpla¹,

Chitsobhuk²

2018

Sci. Res. Essays

View full text Add to dashboard Cite

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.

show abstract