Stationary Wavelet Transform based Image Fusion using fusion rules

,; Radha, Nainavarapu; Babu, Tummala Ranga; ,

doi:10.35940/ijeat.b4110.129219

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Among various types of noise encountered during signal denoising, adaptive white Gaussian noise (AWGN) is the most common. This project thesis primarily focuses on addressing AWGN [8,9] .…”

Section: Overviewmentioning

confidence: 99%

Wavelet transform based image enhancement: A noise reduction approach

Mallaparapu,

Ramarao

2023

Comput. Telecommun. Eng.

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<span lang="EN-US">This paper introduces a new thresholding function that mixes the soft thresholding functions and Smoothly Clipped Absolute Deviation (SCAD) for denoising images using the decimated wavelet transform technique, which is widely popular in various applications. The proposed method is applied to denoise noisy images contaminated with additive white Gaussian noise, employing the Top rule. The efficiency of this new thresholding function is also evaluated within the context of the Translation Invariant method. The results are compared with existing methods such as SCAD, soft thresholding, and the Wiener filter-based denoising approach. Parameters such as Root Mean Square Error (RMSE) and Peak Signal Noise Ratio (PSNR) are employed to assess the quality of denoising.</span>

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“…Among various types of noise encountered during signal denoising, adaptive white Gaussian noise (AWGN) is the most common. This project thesis primarily focuses on addressing AWGN [8,9] .…”

Section: Overviewmentioning

confidence: 99%

Wavelet transform based image enhancement: A noise reduction approach

Mallaparapu,

Ramarao

2023

Comput. Telecommun. Eng.

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An Efficient Cancer Classification Model for CT/MRI/PET Fused Images

Srimathi

Yamuna

Nanmaran

2021

CMIR

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Objective: Image fusion-based cancer classification models used to diagnose cancer and assessment of medical problems in earlier stages that help doctors or health care professionals to plan the treatment plan accordingly. Methods : In this work, a novel Curvelet transform-based image fusion method is developed. CT and PET scan images of benign type tumors fused together using the proposed fusion algorithm and the same way MRI and PET scan images of malignant type tumors fused together to achieve the combined benefits of individual imaging techniques. Then the Marker controlled watershed Algorithm applied on fused image to segment cancer affected area. The various color features, shape features and texture-based features extracted from the segmented image. Then a data set formed with various features, which have given as input to different classifiers namely neural network classifier, Random forest classifier, K-NN classifier to determine the nature of cancer. The results of the classifier will be Normal, Benign or Malignant category of cancer. Results: The performance of the proposed fusion Algorithm compared with existing fusion techniques based on the parameters PSNR, SSIM, Entropy, Mean and Standard Deviation. Curvelet transform based fusion method performs better than already existing methods in terms of five parameters. The performances of classifiers are evaluated using three parameters Accuracy, Sensitivity, and Specificity. K-NN Classifier performs better when compared to the other two classifiers and it provides overall accuracy of 94%, Sensitivity of 88% and Specificity of 84%. Conclusion: The proposed Curvelet transform based image fusion method combined with the K-NN classifier provides better results when compared to other two classifiers when two input images used individually.

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Optimal Image Steganography Content Destruction Techniques

Ameen

Al-Badrany

2021

International Journal of Computers and Communications

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The paper presents two approaches for destroying steganogrphy content in an image. The first is the overwriting approach where a random data can be written again over steganographic images whereas the second approach is the denoising approach. With the second approach two kinds of destruction techniques have been adopted these are filtering and discrete wavelet techniques. These two approaches have been simulated and evaluated over two types of hiding techniques, Least Significant Bit LSB technique and Discrete Cosine Transform DCT technique. The results of the simulation show the capability of both approaches to destroy the hidden information without any alteration to the cover image except the denoising approach enhance the PSNR in any received image even without hidden information by an average of 4dB.

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Stationary Wavelet Transform based Image Fusion using fusion rules

Cited by 3 publications

References 10 publications

Wavelet transform based image enhancement: A noise reduction approach

Wavelet transform based image enhancement: A noise reduction approach

An Efficient Cancer Classification Model for CT/MRI/PET Fused Images

Optimal Image Steganography Content Destruction Techniques

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