Wiener Filter Based on the Image to Blur

Image restoration, refers to the removal or loss in the process of getting digital image degradation of the image quality, image restoration technology is the key to meet the requirements of the point spread function, degradation model is an ill-posed integral equations, in the frequency domain, when H ( U, V ) less or equal to zero, the noise will be amplified, the degraded image and interference in H ( U, V ) value of the spectrum will be small to restore the image influence. In view of the point spread function put forward Wiener filtering algorithm, the noise lead to ill-posed integral with specified signal-to-noise ratio to reduce image restoration effects, through the IPT toolbox for fuzzy image restoration, image quality to achieve the anticipated effect.

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Exploring the Parameter Space of Point Spread Function Determination for the Scanning Electron Microscope—Part II: Effect on Image Restoration Quality

Microscopy and Microanalysis ◽

10.1017/s1431927619014831 ◽

2019 ◽

Vol 25 (05) ◽

pp. 1183-1194

Author(s):

Mandy C. Nevins ◽

Richard K. Hailstone ◽

Eric Lifshin

Keyword(s):

Particle Size ◽

Electron Microscope ◽

Image Quality ◽

Scanning Electron Microscope ◽

Image Restoration ◽

Point Spread Function ◽

Background Correction ◽

Point Spread ◽

Spread Function ◽

Scanning Electron

AbstractPoint spread function (PSF) deconvolution is an attractive software-based technique for resolution improvement in the scanning electron microscope (SEM) because it can restore information which has been blurred by challenging operating conditions. In Part 1, we studied a modern PSF determination method for SEM and explored how various parameters affected the method's ability to accurately estimate the PSF. In Part 2, we extend this exploration to PSF deconvolution for image restoration. The parameters include reference particle size, PSF smoothing (K), background correction, and restoration denoising (λ). Image quality was assessed by visual inspection and Fourier analysis. Overall, PSF deconvolution improved image quality. Low λ enhanced image sharpness at the cost of noise, while high λ created smoother restorations with less detail. λ should be chosen to balance feature preservation and denoising based on the application. Reference particle size within ±0.9 nm and K within a reasonable range had little effect on restoration quality. Restorations using background-corrected PSFs had superior quality compared with using no background correction, but if the correction was too high, the PSF was cut off causing blurrier restorations. Future efforts to automatically determine parameters would remove user guesswork, improve this method's consistency, and maximize interpretability of outputs.

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An Approach to Measuring the Point Spread Function of the Confocal Raman Microscope

Applied Spectroscopy ◽

10.1177/0003702820942599 ◽

2020 ◽

Vol 74 (10) ◽

pp. 1230-1237

Author(s):

Xiang Ding ◽

Yanzhe Fu ◽

Jiyan Zhang ◽

Yao Hu ◽

Shihang Fu

Keyword(s):

Image Quality ◽

Size Effect ◽

Point Spread Function ◽

Performance Indicator ◽

Signal To Noise Ratio ◽

Polystyrene Microspheres ◽

Point Spread ◽

Spread Function ◽

Confocal Raman

The confocal Raman microscope (CRM) is a powerful tool in analytical science. Image quality is the most important performance indicator of CRM systems. The point spread function (PSF) is one of the most useful tools to evaluate the image quality of microscopic systems. A method based on a point-like object is proposed to measure the PSF of CRM, and the size effect of spherical objects is discussed. A series of phantoms are fabricated by embedding different sizes of polystyrene microspheres into polydimethylsiloxane matrix. The diameters of microspheres are from 0.2 µm to 5 µm. The phantoms are tested by measuring the PSF of a commercial CRM whose nominal lateral resolution is about 1 µm. Results of the PSF are obtained and the accuracy of resolution is used to evaluate the size effect of the microspheres. Experimental results are well consistent with theoretical analysis. The error of the PSF can be decreased by reducing the diameter of the microsphere but meanwhile the signal-to-noise ratio (S/N) will be lowered as well. The proper diameter of microspheres is proposed in consideration of the trade-off between the S/N and the measurement error of the PSF. Results indicate that the method provides a useful approach to measurement of the PSF and the resolution of the CRM.

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A Research on the Optimization of Fuzzy Image

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.409-410.1593 ◽

2013 ◽

Vol 409-410 ◽

pp. 1593-1596

Author(s):

Xue Feng Wu ◽

Yu Fan

Keyword(s):

Mathematical Model ◽

Image Restoration ◽

Point Spread Function ◽

Motion Blur ◽

Inverse Filtering ◽

Point Spread ◽

Spread Function ◽

Blurred Image ◽

Blurred Images ◽

Fuzzy Image

The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given According to the characteristics of blurred images the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration, Lucy-Richardson image restoration and Wiener image restoration. The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved, and the image restoration is more stable.

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Blind Image Restoration Based on Signal-to-Noise Ratio and Gaussian Point Spread Function Estimation

Research Journal of Applied Sciences Engineering and Technology ◽

10.19026/rjaset.5.4830 ◽

2013 ◽

Vol 5 (4) ◽

pp. 1149-1153 ◽

Cited By ~ 1

Author(s):

Fengqing Qin

Keyword(s):

Image Restoration ◽

Point Spread Function ◽

Signal To Noise Ratio ◽

Function Estimation ◽

Signal To Noise ◽

Blind Image Restoration ◽

Point Spread ◽

Spread Function ◽

Blind Image ◽

Gaussian Point

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A Contribution to Sonograph Image Quality Estimation Using Point Spread Function

Acta Medica (Hradec Kralove Czech Republic) ◽

10.14712/18059694.2018.109 ◽

2004 ◽

Vol 47 (4) ◽

pp. 293-295

Author(s):

Ladislav Doležal ◽

Jan Hálek

Keyword(s):

Image Quality ◽

Point Spread Function ◽

Signal To Noise Ratio ◽

Region Of Interest ◽

Measuring System ◽

Point Spread ◽

Scanning Area ◽

Spread Function ◽

Gain Compensation

In medical sonography, sonograph image quality is an essential aspect for the safety of both patient and doctor. Its evaluation therefore requires an accurate and objective method for measurement. In this regard, a number of methods are in current use. Most of these are based on tissue mimicking phantom imaging. In contrast, we have used another principle based on Point Spread Function (PSF) analysis which is a product of the measuring system we have developed. In this case, the measured sonograph scans a small metallic ball target that moves in a water bath on a specified trajectory. The Region Of Interest (ROI) of the sonogram containing the ball target picture is digitised and the amplitude of the pixels analysed. The result is the PSF from which we calculate the lateral resolution (LR). For this purpose, we use our own original software. Using this method, we have to date been able to plot LR characteristics over the scanning plane. The method allows us to differentiate separate scanning lines and even multiple focal areas for dynamic focussing systems. It can detect malfunctions in dynamic focussing, size of aperture, time gain compensation function and/or transducer element failure. The procedure itself is not as easy or as fast to use as tissue mimicking phantoms or 3D signal to noise ratio evaluation, but it provides accurate and objective numeric parameters corresponding to the quality of image at any specified point over the whole scanning area. It is also a very powerful tool when used in combination with the other methods mentioned above.

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