Review and application of different contrast enhancement technique on various images

Meena, Sunil Kumar; Potnis, Anjali; Mishra, Madhuram; Dwivedy, Prashant; Soofi, Shahbaz

doi:10.1109/iementech.2017.8076993

Cited by 5 publications

(1 citation statement)

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“…[7] traditional image enhancement methods represent a traditional approach in computer vision to image enhancement, whether it involves adjustments in the spatial domain or the frequency domain. Examples of such methods are the graph equation, the covariance-limited adaptive graph equation (kalbi), Gamow's modulation, and Gaussian filtering, among others [8]. They are processed [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Image Enhancement using Convolution Neural Networks

Salman,

Kalakech

2024

Babylonian Journal of Machine Learning

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The research presents a comprehensive exploration of the topic of image enhancement using convolutional neural networks (CNN).The research goes deeper into the advanced field of image processing based on the use of neural networks to automatically and efficiently improve the quality and detail of images. The thesis shows that convolutional neural networks are one of the types of deep neural networks, which are specially designed to gain knowledge from big data and extract complex features and patterns found in images. The different layers of the grid are discussed in detail, dealing with images incrementally and extracting different attributes in each layer. The research also highlights CNN's ability to detect, learn and improve important details found in images through convolutions, filtering and data aggregation processes. The proposed CNN image enhancement model was developed and tested on both medical and normal images. The images were optimized using the proposed model and compared with other models. Various quality measures were used to evaluate the results. The results showed that the proposed model can significantly improve the quality of images.

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Section: Literature Reviewmentioning

confidence: 99%