Image Restoration by Using Evolutionary Technique to Denoise Gaussian and Impulse Noise

Nallaperumal, Krishnan; Muthukumar, S.; Subban, Ravi; D, Shashikala; Pasupathi, P.

doi:10.1007/978-3-319-03844-5_31

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…GA can be hybridized with fuzzy logic to denoise the noisy image. GA based restoration technique can be used to remove haze, fog and smog from the given image [8,110,146,200]. Object detection and recognition is a challenging issue in realworld problem.…”

Section: Image Processingmentioning

confidence: 99%

A review on genetic algorithm: past, present, and future

Katoch

Chauhan

Kumar

2020

Multimed Tools Appl

2,700

761

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In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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Section: Image Processingmentioning

confidence: 99%

A review on genetic algorithm: past, present, and future

Katoch

Chauhan

Kumar

2020

Multimed Tools Appl

2,700

761

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“…In this case the problem is ill-posed but can still be solved if an independent criterion for "good focus" is available against which the success of the solution can be evaluated. In such so-called blind deconvolution schemes a systemic search based on, for example, a genetic algorithm (GM) [10], can be conducted to identify the combination of deblurred images and uniform PSFs that best reproduces the recorded blurred image. Solutions obtained in this way are generally not unique but may still be useful.…”

Section: Image Deblurringmentioning

confidence: 99%

Numerical Focusing of a Wide-Field-Angle Earth Radiation Budget Imager Using an Artificial Neural Network

et al. 2020

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Narrow field-of-view scanning thermistor bolometer radiometers have traditionally been used to monitor the earth’s radiant energy budget from low earth orbit (LEO). Such instruments use a combination of cross-path scanning and along-path spacecraft motion to obtain a patchwork of punctual observations which are ultimately assembled into a mosaic. Monitoring has also been achieved using non-scanning instruments operating in a push-broom mode in LOE and imagers operating in geostationary orbit. The current contribution considers a fourth possibility, that of an imager operating in LEO. The system under consideration consists of a Ritchey-Chrétien telescope illuminating a plane two-dimensional microbolometer array. At large field angles, the focal length of the candidate instrument is field-angle dependent, resulting in a blurred image in the readout plane. Presented is a full-field focusing algorithm based on an artificial neural network (ANN). Absorbed power distributions on the microbolometer array produced by discretized scenes are obtained using a high-fidelity Monte Carlo ray-trace (MCRT) model of the imager. The resulting readout array/scene pairs are then used to train an ANN. We demonstrate that a properly trained ANN can be used to convert the readout power distribution into an accurate image of the corresponding discretized scene. This opens the possibility of using an ANN based on a high-fidelity imager model for numerical focusing of an actual imager.

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“…Image noise level estimation (NLE) is an important technique in computer image processing because of its huge importance in object detection [1], [2], denoising [3], [4], and super-resolution [5] and it has been studied for several decades [6], [7]. Normal single image denoising methods typically need pre-known arguments of the noise distribution model [8], [9], which has a huge impact on the performance of the denoising algorithm, so NLE becomes a key point.…”

Section: Introductionmentioning

confidence: 99%

Image Noise Level Estimation for Rice Noise Based on Extended ELM Neural Network Training Algorithm

Yang

et al. 2019

IEEE Access

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The estimation of image noise level is a critical task for image denoising or super-resolution reconstruction. Mathematical methods like patch-based or model-based methods, suffer from the sensitivity of the selection of homogeneous regions or the selection of a proper statistic model, leading to inaccurate estimation, especially in signal-dependent noise cases, such as Rice noise. Ordinary, fully connected networks often suffer from the over-fitting problem, restricting their usage for realistic images. This article proposes a deep-learning-based algorithm by building a deep neural network, and train it by using the evolutionary genetic algorithm and extreme learning machine (ELM) algorithm extended into Hinton's dropout framework. By combining the evolutionary genetic algorithm and the proposed extended ELM algorithm, comparative results are obtained, showing higher accuracy and better stability than several stateof-the-art algorithms.INDEX TERMS Noise level estimation, deep learning, convolution neural network, extreme learning machine, dropout.

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Image Restoration by Using Evolutionary Technique to Denoise Gaussian and Impulse Noise

Cited by 3 publications

References 16 publications

A review on genetic algorithm: past, present, and future

A review on genetic algorithm: past, present, and future

Numerical Focusing of a Wide-Field-Angle Earth Radiation Budget Imager Using an Artificial Neural Network

Image Noise Level Estimation for Rice Noise Based on Extended ELM Neural Network Training Algorithm

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