A hybrid image similarity measure based on a new combination of different similarity techniques

Hamza, Nisreen Ryadh; Dihin, Rasha Ail; Abdulameer, Mohammed Hasan

doi:10.11591/ijece.v10i2.pp1814-1822

Cited by 2 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The tool utilized for directing the investigation is MATLAB R2016a. The conventional visual quality estimate measures [29] peak signal to noise ratio (PSNR), structural similarity index (SSIM), and Histogram are utilised to assess the efficacy of the results produced. The accuracy of the proposed scheme is shown by a histogram plot comparison of the original and restored images.…”

Section: Resultsmentioning

confidence: 99%

Impulse noise recuperation from grayscale and medical images using supervised curve fitting linear regression and mean filter

Thomas

Krishna

2022

IJEECS

View full text Add to dashboard Cite

Acquisition of images from electronic devices or Transmission of the image through any medium will cause an additional commotion. This study aims to investigate a framework for eliminating impulse noise from grayscale and medical images by utilizing linear regression and a mean filter. Linear regression is a supervised machine learning algorithm that computes the value of a dependent variable based on an independent variable. The value of the recuperating pixel is measured using a curve-fitting, direction-based linear regression approach or applying a mean filter to the noise-free pixels. The efficiency of the proposed technique experiments with benchmark test images and the images of the USC-SIPI and TESTIMAGES data sets. Peak signal-to-noise ratio and structural similarity index metrics are determined to prove the performance of the proposed method. The evaluated results, when compared with the seven recent state-of-the-art techniques, show the superiority of the proposed method in terms of visual quality and accuracy. The proposed model achieves an average PSNR value of 65.21dB and an SSIM value of 0.999 for the reconstruction of medical images, proving its accuracy and efficiency. The impulse noise restoration process helps the radiologist get a clear visual clarity of the DICOM image for diagnosis purposes.

show abstract

Section: Resultsmentioning

confidence: 99%

Impulse noise recuperation from grayscale and medical images using supervised curve fitting linear regression and mean filter

Thomas

Krishna

2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…The structural similarity index measure (SSIM) [15], [22], [23] is a perceptual measure that compares image pixel severity style based on local luminance and pixel disparity. Let x and y be two data vectors which should only contain non-negative values and represent the pixel values to be compared with.…”

Section: Resultsmentioning

confidence: 99%

Lifting dual tree complex wavelets transform

Naimi

Adamou-Mitiche

Mitiche

2021

IJECE

View full text Add to dashboard Cite

We describe the lifting dual tree complex wavelet transform (LDTCWT), a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). We describe a way to estimate the accuracy of this approximation and style appropriate filters to attain this. These benefits are often exploited among applications like denoising, segmentation, image fusion and compression. The results of applications shrinkage denoising demonstrate objective and subjective enhancements over the dual tree complex wavelet transform (DTCWT). The results of the shrinkage denoising example application indicate empirical and subjective enhancements over the DTCWT. The new transform with the DTCWT provide a trade-off between denoising computational competence of performance, and memory necessities. We tend to use the PSNR (peak signal to noise ratio) alongside the structural similarity index measure (SSIM) and the SSIM map to estimate denoised image quality.

show abstract

A hybrid image similarity measure based on a new combination of different similarity techniques

Cited by 2 publications

References 11 publications

Impulse noise recuperation from grayscale and medical images using supervised curve fitting linear regression and mean filter

Impulse noise recuperation from grayscale and medical images using supervised curve fitting linear regression and mean filter

Lifting dual tree complex wavelets transform

Contact Info

Product

Resources

About