Image enhancement based on edge boosting algorithm

Ngernplubpla, Jaturon; Chitsobhuk, Orachat

doi:10.1117/12.2228234

Cited by 4 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Edge sketch image (ESI) was the new edge emphasizing algorithm proposed in Ngernplubpla and Chitsobhuk (2015). This technique used Maximum Likelihood Estimation (MLE) (Montgomery and Runger, 2010) for estimating a weight (WML) from LR image and enhanced the edges in the high frequency area as shown in Equations 2 and 3.…”

Section: Edge Sketch Imagementioning

confidence: 99%

“…The first experiment is to examine how to integrate the different gradient profile clustering to recover the distorted pixels in the LR image. Three techniques: Low-High (LH) (Ngernplubpla and Chitsobhuk, 2013), Edge Boosting Algorithm (EBA) (Ngernplubpla and Chitsobhuk, 2015), and the proposed algorithm using neuro-fuzzy gradient profile with GWSF is conducted and the result is presented in Table 4.…”

Section: Image Enhancement Performancementioning

confidence: 99%

“…However, the weights were learned from limited number of samples thus led to unsuccessful results in some cases. Therefore, in Ngernplubpla and Chitsobhuk (2015), image enhancement was proposed based on edge boosting algorithm (EBA) using Priority Map Generation. The Priority Map was generated as regularization on how the gradient priors should influence the enhancement of the LR image.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Section: Edge Sketch Imagementioning

confidence: 99%

Section: Image Enhancement Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation