Improving image-quality of interference fringes of out-of-plane vibration using temporal speckle pattern interferometry and standard deviation for piezoelectric plates

2013 ◽  
Vol 60 (7) ◽  
pp. 1412-1423 ◽  
Author(s):  
Chien-Ching Ma ◽  
Ching-Yuan Chang
Keyword(s):  
Standard Deviation ◽  
Image Quality ◽  
Speckle Pattern ◽  
Plane Vibration ◽  
Interference Fringes ◽  
Out Of Plane ◽  
Piezoelectric Plates

scholarly journals Automated Universal Image Quality Index Measurement vs. Automated Noise Measurement: Which Method is Better to Define CT Image Quality?

2019 ◽  
Vol 9 (2) ◽  
pp. 132
Author(s):  
Fauzia Puspa Lestari ◽  
Choirul Anam ◽  
Yati Hardiyanti ◽  
Freddy Haryanto
Keyword(s):  
Standard Deviation ◽  
Image Quality ◽  
Treatment Planning ◽  
Quality Index ◽  
Hounsfield Unit ◽  
Noise Measurement ◽  
Ct Image ◽  
Index Measurement ◽  
Image Quality Index

Automatitation method in defining the quality of CT image is needed to optimize CT Scan treatment planning. So, the optimization of treatment planning can also be done automatically. There are various methods proposed to define the quality of an image. The purpose of this study was to find the simple and precision method to define CT image. We compared the performance of Automated Noise Measurement (ANM) and Automated Universal Image Quality Index (UIQI). We also compared them with the Manual noise measurement method based on the level of convergence in homogeneous images. The first step of Automated Noise Measurement was to create binary density slice using threshold values. Then, a masked image was performed by masking the original image and binary image. The standard deviation of every pixel for a certain kernel size was calculated by using a sliding window operation. The fourth step was to make a noise histogram from the noise map and determine the final noise in the image as the histogram peak. Then this calculation was normalized by the peak of the Hounsfield Unit (HU) histogram. All these steps were done with various kernel sizes for different slices in-homogenous phantom. In the Automatic UIQI method, the steps in the ANM method are carried out until the masked image stage, then UIQI is calculated for the masked image. The results show that automatic UIQI was more convergence in defining image quality than manual noise measurement and automated noise measurement by the lowest standard deviation which was only 0.00032867.

Observability of Very Thick Specimen by Using Transmission Scanning Electron Microscope

1971 ◽  
Vol 29 ◽  
pp. 26-27
Author(s):  
K. Shibatomi ◽  
T. Yamanoto ◽  
H. Koike
Keyword(s):  
Electron Microscope ◽  
Image Quality ◽  
Scanning Electron Microscope ◽  
Chromatic Aberration ◽  
Image Contrast ◽  
Objective Lens ◽  
Thick Specimen ◽  
Transmission Electron ◽  
Scanning Electron

In the observation of a thick specimen by means of a transmission electron microscope, the intensity of electrons passing through the objective lens aperture is greatly reduced. So that the image is almost invisible. In addition to this fact, it have been reported that a chromatic aberration causes the deterioration of the image contrast rather than that of the resolution. The scanning electron microscope is, however, capable of electrically amplifying the signal of the decreasing intensity, and also free from a chromatic aberration so that the deterioration of the image contrast due to the aberration can be prevented. The electrical improvement of the image quality can be carried out by using the fascionating features of the SEM, that is, the amplification of a weak in-put signal forming the image and the descriminating action of the heigh level signal of the background. This paper reports some of the experimental results about the thickness dependence of the observability and quality of the image in the case of the transmission SEM.

Organ doses and subjective image quality of indirect digital panoramic radiography

2001 ◽  
Vol 30 (6) ◽  
pp. 308-313 ◽  
Author(s):  
F Gijbels ◽  
G Sanderink ◽  
C Bou Serhal ◽  
H Pauwels ◽  
R Jacobs
Keyword(s):  
Image Quality ◽  
Panoramic Radiography ◽  
Organ Doses ◽  
Subjective Image Quality ◽  
Digital Panoramic Radiography

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

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