Evaluation of structural similarity quality measures for wrapped recovered phase maps in optical metrology

Etchepareborda, Pablo; Bianchetti, Arturo; Vadnjal, Ana Laura; Federico, Alejandro

doi:10.1016/j.optlaseng.2019.01.004

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…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

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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.

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Section: Resultsmentioning

confidence: 99%

Lifting dual tree complex wavelets transform

Naimi

Adamou-Mitiche

Mitiche

2021

IJECE

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“…where µ x and µ y are the mean values of the noiseless image x(i, j) and the denoised image y(i, j), σ x and σ y are their variances and σ xy their covariance, constant C 1 = (0.01 × 255) 2 = 6.5025, and constant C 2 = (0.03 × 255) 2 = 58.5225 as suggested [34,35]. The value of SSIM is included in the interval (−1, +1).…”

Section: Simulationmentioning

confidence: 99%

Fast phase denoising using stationary wavelet transform in speckle pattern interferometry

Ning

et al. 2019

Meas. Sci. Technol.

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Phase fringe patterns in speckle pattern interferometry need to be denoised before they are processed further to ensure phase unwrapping reliability and phase determination accuracy. A fast phase denoising method combining the sine-cosine transform and the stationary wavelet transform, with a detailed, unified and adaptive parameter setting of the wavelet function, decomposition level, threshold selection and threshold function, is proposed for a speckle interference image. Owing to the ability of the sine-cosine transform to retain an abrupt phase change, and the stationary wavelet transform to quickly remove the phase noise, the proposed approach can be used to denoise the phase fringe pattern effectively and quickly. These characteristics are verified by simulation as well as practical experiments. Compared with the popular phase denoising method-the windowed Fourier transform with a window size of 20-the proposed method can save the running time by at least 16 times in a practical application test. This speed advantage will be more important when a high-resolution camera is used in dynamic measurement.

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“…Structural similarity, this method is to use the three indicators of structural similarity, which is the combination of brightness, contrast and structure of the image as the assessment index of the degree of blur between the image to be assessed and the reference image. Specifically, for digital images, the average gray scale is used to estimate the brightness, and the standard deviation is used to estimate the contrast, the structural information will be calculated and these formulas are as follows [25]:…”

Section: Structural Similaritymentioning

confidence: 99%

Study on Holographic Image Recognition Technology of Zooplankton

Shi¹,

Wang²,

Cao³

et al. 2019

dtcse

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Marine zooplankton has important ecological and economic value. The observation and automatic image recognition technology of marine zooplankton is an important mean to acquire data such as species, quantity, spatial distribution and behavioral postures of zooplankton, and is an important support for marine scientific research. Digital holography has an innate advantage of refocusing and reconstruction, which is suitable for deep learning and living zooplankton recognition. In this study, a large number of holographic images was trained by using the improved YOLOv2 model, and after test, the study achieved satisfactory results: the models trained by the images with sharpness assessment score of 0.6 or higher, have precision rate above 94% and a recall rate above 88%. This study mainly discusses: (1) the detection method of moving targets to acquire the images of moving zooplankton; (2) the two factors that affect the holographic images recognition results, mean (pixel mean of images) subtraction operation and image sharpness, and the no-reference sharpness assessment based on structural similarity for holographic images; (3) the relationship between sharpness assessment index or mean subtraction and the recognition results.

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Evaluation of structural similarity quality measures for wrapped recovered phase maps in optical metrology

Cited by 8 publications

References 16 publications

Lifting dual tree complex wavelets transform

Lifting dual tree complex wavelets transform

Fast phase denoising using stationary wavelet transform in speckle pattern interferometry

Study on Holographic Image Recognition Technology of Zooplankton

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