A Survey of Image Enhancement and Object Detection Methods

Parekh, Jinay; Turakhia, Poojan; Bhinderwala, Hussain; Dhage, Sudhir

doi:10.1007/978-981-15-4409-5_91

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…It is suggested that deep learning, which is essentially a neural network with layers, is one of the effective and emerging techniques for enhancement of those images. [ 41 , 42 , 43 , 44 ] It is interesting to find out that it is strongly depended on the training datasets. Therefore, it can be used to process various low‐quality images generated by the sophisticated environment that is associated with low illumination levels, strong color deviations, complex artifacts, high‐level noise, etc.…”

Section: Discussionmentioning

confidence: 99%

Image Enhancement via Special Functions and Its Application for Near Infrared Imaging

et al. 2023

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Image enhancement is important given that it can be used to highlight the area of interest in the images. This article designs four filters via special function for realizing image enhancement. Firstly, a filter based on the exponential function is designed. When the value of the progression is even, the edge feature can be extracted. When the value of the progression is odd, sharp contrast can be obtained. Secondly, a filter is built using hyperbolic cosine and its inverse function, where a printmaking feature can be extracted. Thirdly, a filter is made via a hyperbolic secant function and its inverse. It can lead to the extraction of image edge. When the progression value is increasing, marginal effect can be found and the brightness is decreasing. Ripple morphology can be found. Fourthly, a filter is constructed through a hyperbolic sine function and its inverse, where marginal features can be extracted. Furthermore, these filters are useful for extracting the marginal features even when a high noise density of 0.9 is added to the original images. They are useful for highlighting the images acquired from near infrared imaging.

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Section: Discussionmentioning

confidence: 99%

Image Enhancement via Special Functions and Its Application for Near Infrared Imaging

et al. 2023

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“…For symmetric exponential filters, the ISEF filter generates the GEF and SDEF from the first and second derivatives of the operator. The (7) is the normalized symmetric exponential filter in one dimension:…”

Section: Infinite Symmetrical Exponential Filter (Isef)mentioning

confidence: 99%

Improving the iterative back projection estimation through Lorentzian sharp infinite symmetrical filter

Rahim

Yaakob

Lee

et al. 2022

IJECE

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<span>This study proposed an enhancement technique for improvising the estimation technique in iterative back projection (IBP) by using the Lorentzian error function with a sharp infinite symmetrical filter (SISEF). The IBP estimation is an iteratively based error correction that can minimize the error reconstruction significantly. However, the IBP has a drawback in that it suffers from jaggy and ringing artifacts as a result of the iterative reconstruction method and the absence of edge guidance. Furthermore, because the IBP estimator tended to oscillate at the same solution frequently, numerous iterations were required. Therefore, this study proposed edge enhancement to enhance the estimator by using the combination of the IBP with Lorentzian SISEF to produce a finer high-resolution output image. As a result, the SISEF is used to improvise the estimator by providing high accuracy of edge detail information for enhancing the edge image. At the same time, the Lorentzian error norm helps to increase the robustness of the IBP algorithm from contamination of additional noise and the ringing artifacts.</span>

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“…For example, ML models are now an integral part of the image-processing workflow in cell phone photography, particularly because ML models are well suited for image analysis and processing. 41,42 In particular, conditional generative adversarial neural networks (cGANNs) have been developed as a general tool for image-to-image translation from an input domain to an output domain. 43−45 cGANNs are perfectly suited to image processing applications such as removing artifacts, reducing noise, or generating missing features.…”

Section: ■ Introductionmentioning

confidence: 99%

“…ML provides a general approach to extract features from large data sets without preset physical models. − Indeed, ML implementations are now commonplace, not only in science, but ML has transformed nearly all modern consumer electronics. For example, ML models are now an integral part of the image-processing workflow in cell phone photography, particularly because ML models are well suited for image analysis and processing. , In particular, conditional generative adversarial neural networks (cGANNs) have been developed as a general tool for image-to-image translation from an input domain to an output domain. − cGANNs are perfectly suited to image processing applications such as removing artifacts, reducing noise, or generating missing features. In short, the cGANN architecture is composed of a generator convolutional neural network, which generates sets of images, and a corresponding discriminator, which distinguishes between images derived from the training set versus the generated image set (Figure ).…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Neural Networks for Denoising Coherent Multidimensional Spectra

Al-Mualem

Baiz

2022

J. Phys. Chem. A

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Ultrafast spectroscopy often involves measuring weak signals and long data acquisition times. Spectra are typically collected as a "pump−probe" spectrum by measuring differences in intensity across laser shots. Shot-to-shot intensity fluctuations are most often the primary source of noise in ultrafast spectroscopy. Here, we present a novel approach for denoising ultrafast twodimensional infrared (2D IR) spectra using conditional generative adversarial neural networks (cGANNs). The cGANN approach is able to eliminate shot-toshot noise and reconstruct the line shapes present in the noisy input spectrum. We

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A Survey of Image Enhancement and Object Detection Methods

Cited by 8 publications

References 13 publications

Image Enhancement via Special Functions and Its Application for Near Infrared Imaging

Image Enhancement via Special Functions and Its Application for Near Infrared Imaging

Improving the iterative back projection estimation through Lorentzian sharp infinite symmetrical filter

Generative Adversarial Neural Networks for Denoising Coherent Multidimensional Spectra

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