Dynamic properties of feed-forward neural networks and application in contrast enhancement for image

Zhang, Chunrui; Zhang, Xianhong; Zhang, Yazhou

doi:10.1016/j.chaos.2018.07.016

Cited by 7 publications

(1 citation statement)

References 23 publications

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“…Literature [18] generated Delaunay graphs to remove outliers and selected candidate outliers by comparing the differences in Delaunay structure. To improve accuracy, [19] added scale, direction, and distance constraints to the matching points. e RANSAC algorithm was improved by [20].…”

Section: Related Workmentioning

confidence: 99%

Image Contrast Enhancement Method Based on Nonlinear Space and Space Constraints

Zhao¹,

Xia²

2022

Wireless Communications and Mobile Computing

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Remote sensing, environmental monitoring, pattern recognition, and other fields all use this critical and indispensable basic technology. The article proposes an image contrast enhancement detection algorithm based on a linear model to address the problem that the current global contrast enhancement detection algorithm does not have high classification accuracy under the low-intensity JPEG compression quality factor. To decompose the original image, use the biorthogonal wavelet transform. The improved fuzzy set enhancement algorithm is used for the low-frequency subband coefficient. The results obtained after simulating this algorithm show that it is very effective in improving contrast, enhancing image details, and suppressing noise. It has the ability to greatly improve the image’s visual effect, as well as the advantages of parameter adaptation and high algorithm efficiency.

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Section: Related Workmentioning

confidence: 99%