Comparison of DWT, DWT-SWT, and DT-CWT for low resolution satellite images enhancement

Hemalatha, M.; Varadarajan, S.; Muralimohanbabu, Y.

doi:10.1109/icammaet.2017.8186662

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Typically, the high pass co-efficient values contain the fluctuations and the low pass components contain the original information of the image [21]. The decomposition of the images using wavelet transform can be done as a decomposition tree in which each decomposition level would yield the approximate coefficient values, the detailed co-efficient values, the horizontal co-efficient values and the vertical co-efficient values [22], [23]. Thus the image in the spatial domain would be converted to the wavelet domain co-efficients as [24].…”

Section: Image Pre-processingmentioning

confidence: 99%

A multi-instance learning based approach for whitefly pest detection

Chand

Dhiman

Singh

2023

IJEECS

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Agriculture constantly faces various challenges including attacks from new pests and insects. With large farm sizes and plummeting manpower in the agricultural sector, it becomes challenging to continuously monitor crops for pest infestation. In this research paper, a specific type of pest attack known as the white fly attack has been investigated which affects a variety of crops. This paper presents four different approaches for automated classification of whiteflies which are the Bayesian network, convolution neural network (CNN), ResNet and multi-instance learning-CNN. A comparative analysis with conventional machine learning and deep learning techniques has also been presented. The performance of the proposed technique has been evaluated in terms of the classification accuracy. The experimental results obtained show that the proposed technique attains a classification accuracy of 95.53%, 96.9%, 97.6% and 98.13% for the four models respectively. A comparative analysis in terms of accuracy of classificaiton, with existing techniques shows that the proposed technique outperforms baseline deep learning models identifying whitefly infestation.

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Section: Image Pre-processingmentioning

confidence: 99%

A multi-instance learning based approach for whitefly pest detection

Chand

Dhiman

Singh

2023

IJEECS

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“…The watermark is subjected to similar processing after being scrambled using Arnold transform. In [14], satellite image is enhanced using three wavelet techniques: discrete wavelet transforms (DWT) -Stationary wavelet transforms (DWT-SWT) and Dual tree complex wavelet transforms (DT-CWT). The result shows that DT-CWT highlight superior performance.…”

Section: Related Workmentioning

confidence: 99%

A novel Watermarking Technique Based on Hybrid Transforms

Abdallah¹,

Alsodairi²

2022

Int. j. commun. netw. inf. secur.

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This paper proposed Anovel watermarking scheme using hybrid of Dual Tree Complex Wavelet Transform and singular value decomposition . Image watermarking is to embed copyright data in image bit streams. Our proposed technique demonestrates the effective and robust of image watermarking algorithms using a hybrid of two strong mathematical transforms; the 2-level Dual Tree Complex Wavelet Transform (DT-CWT) and Singular Value Decomposition (SVD). This technique shows high level of security and robustness against attacks. The algorithm was tested for imperceptibility and robustness and the results were compared with DWT-SVD-based technique, it is shown that the proposed watermarking schemes is considerably more robust and effective.

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“…The dual-tree complex wavelet (DTCWT) is tight frame wavelet in [18,19]. The DTCWT have advantages that orthogonal bases can not have for image de-noising [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Image Denoising Based on Bivariate Distribution

Zhao¹,

Zhao²,

Zhao³

2020

Symmetry

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The literature has shown that the performance of the de-noising algorithm was greatly influenced by the dependencies between wavelet coefficients. In this paper, the bivariate probability density function (PDF) was proposed which was symmetric, and the dependencies between the coefficients were considered. The bivariate Cauchy distribution and the bivariate Student’s distribution are special cases of the proposed bivariate PDF. One of the parameters in the probability density function gave the estimation method, and the other parameter can take any real number greater than 2. The algorithm adopted a maximum a posteriori estimator employing the dual-tree complex wavelet transform (DTCWT). Compared with the existing best results, the method is faster and more efficient than the previous numerical integration techniques. The bivariate shrinkage function of the proposed algorithm can be expressed explicitly. The proposed method is simple to implement.

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Comparison of DWT, DWT-SWT, and DT-CWT for low resolution satellite images enhancement

Cited by 4 publications

References 7 publications

A multi-instance learning based approach for whitefly pest detection

A multi-instance learning based approach for whitefly pest detection

A novel Watermarking Technique Based on Hybrid Transforms

Image Denoising Based on Bivariate Distribution

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