scholarly journals Image Definition Evaluations on Denoised and Sharpened Wood Grain Images

Coatings ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 976
Author(s):  
Jingjing Mao ◽  
Zhihui Wu ◽  
Xinhao Feng
Keyword(s):  
Image Acquisition ◽  
Threshold Value ◽  
Objective Evaluation ◽  
Modulation Transfer ◽  
Image Sharpness ◽  
Evaluation Functions ◽  
Surface Decoration ◽  
Scanned Images ◽  
Wood Grain ◽  
Image Definition

Decorative paper and wood veneer have been widely used in the surface decoration of wood-based panels. These surface decoration methods require two-dimensional image acquisition of natural wood grain to obtain the digital grain. However, optically scanned images sometimes produce noise during the process of image acquisition and transmission. In this situation, scanned images cannot be used directly in wood grain reproductions. To reduce noise and retain or strengthen the image sharpness, studies are mostly aimed at the improvement of classic denoising algorithms and edge width-based sharpness evaluation algorithms. To enhance accessibility for common users, four kinds of wood grain images with distinct colors were chosen, and the noise filter (Dust & Scratches) and sharpen filter (Unsharp Mask, USM) were used to denoise and sharpen the images. According to the properties of the two filters, image definition in this study was considered from two aspects: detail retention and sharpness retention. To have an objective evaluation on the definition of denoised and sharpened images, two types of evaluation functions Roberts gradient function (RGF) and modulation transfer function (MTF) were introduced. The purpose of this study was to estimate the image definition by exploring the relationships between the evaluation functions and the commonly used filters in order to allow the required wood grain images to be quickly and accurately processed by common users. The results showed that RGF was only applicable to the case where the two parameters in Dust & Scratches were changed individually, while MTF was not suitable in any case. When both parameters were changed, the required denoising scheme could be obtained through PSNR and SSIM. For the images with distinct colors, even if they were acquired in the same way, denoising them with the same parameter setting was not recommended. For sharpness retention, the values of Radius and Amount in USM were given, and when the Threshold value was set to 20 (levels), the sharpness of the wood grain images barely changed. In this case, both RGF and MTF were suitable to evaluate the sharpness of the wood grain images sharpened by USM.

scholarly journals Measuring image sharpness using modulation transfer function in magnetic resonance imaging

2018 ◽  
Vol 7 (2.12) ◽  
pp. 115
Author(s):  
Woo Taek Lim ◽  
Ki Jeong Kim ◽  
Dong Hee Hong ◽  
Cheong Hwan Lim ◽  
Hong Ryang Jung
Keyword(s):  
Magnetic Resonance ◽  
Evaluation Method ◽  
Objective Evaluation ◽  
Theoretical Concept ◽  
Modulation Transfer ◽  
Image Sharpness ◽  
Acr Mri Phantom ◽  
The Difference ◽  
Edge Method ◽  
Head Coil

Background/Objectives: The purpose of this study was to propose a method to maintain the objectivity and validity of measuring image sharpness by changes of ETL using MTF in MRI quantitatively and provide fundamental data for future evaluation and management of magnetic resource imaging quality.Methods/Statistical analysis: We conducted phantom test using ACR MRI phantom. ImageJ and OriginPro programs were used for MTF measurement. For MTF measurement using edge method, after achieving ESF by using ImageJ, LSF was calculated by differentiation in OriginPro. Finally, MTF value was obtained through conversion. Image sharpness was defined based on 50% of MTF value.Findings: Results of sharpness measurement by ETL increase revealed that MTF 50% was decreased when ETL was increased. Sharpness comparison between 1ch head coil and 8ch brain coil at 1.5T showed that it was higher for 1ch head coil, although the difference between the two was not statistically significant. However, sharpness of 1ch head coil at 3.0T MRI was found to be significantly higher than that of 8ch brain coil at 3.0T MRI.Improvements/Applications: This study confirms the theoretical concept that MTF measured by ACR standard phantom can be used as a quantitative evaluation method for spatial resolution in the magnetic resonance medical image quality management. It can be considered a meaningful objective evaluation method.  

Normalized Acquisition System of the Facial Diagnosis in Traditional Chinese Medicine

2013 ◽  
Vol 291-294 ◽  
pp. 2834-2844
Author(s):  
Fu Feng Li ◽  
Peng Qian ◽  
Xiao Yan Zheng ◽  
Yi Qin Wang ◽  
Zhu Mei Sun ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Information Acquisition ◽  
Light Emitting Diode ◽  
Image Acquisition ◽  
Objective Evaluation ◽  
Acquisition System ◽  
Natural Light ◽  
Before And After ◽  
Standard Color

Background: Facial diagnosis, an important part of clinical diagnosis in Traditional Chinese Medicine (TCM), is a method used to diagnose the functions of Zang-Fu organs by observing the color, luster, shape, and texture of faces. However, the traditional facial diagnosis mainly relies on doctors’ eyes, languages, and personal clinical experiences. Results are not only determined based on the doctors’ diagnostic skills but also by external conditions such as light and temperature. Without objective evaluation criteria, conducting studies on facial diagnosis to widen its application are difficult. To solve this problem, we should find new methods and technologies to realize the objectification and normalization of diagnosis in TCM. In this article, we discuss the results of our study on the normalized acquisition system of facial diagnosis in TCM. Some of the hardware used includes lights, image acquisition equipment, and dark boxes. The software used includes image acquisition and preprocessing. To the best of our knowledge, this study is the first to propose this system and no similar study has been reported yet. Methods: We initially introduced the hardware and the software that we used in this study. The key technologies in this system, including lighting equipment, facial diagnosis device, facial information acquisition scales, image acquisition, and preprocessing were then introduced one by one. The hardware of this system consists of a light emitting diode (LED), a digital camera, a dark box, and a computer. Each of this hardware has its special function: the LED imitates natural light; the camera records facial images; the dark box imitates the consultation room; and the computer stores the images. The software is used to acquire and adjust the images. The image acquisition system uses the computer to control the opening and the closing of the camera, the photography, and the setup of relevant index to obtain the fully automatic photography of faces and information transmission. Results: The normalized acquisition system of facial diagnosis in TCM was tested according to the following procedures. (1) The lighting uniformity of acquisition windows was tested. Results of the uniformity test showed an even distribution of the illumination on the opening of the dark box for facial complexion collection. (2) TCM experts valuated the acquisition environment. The four features basically tallied among the different diagnoses of doctors, in which the lowest consistency was 75% for the lip color and the highest was 95% for the moist/dry lips. These data showed that the LED the natural light can be used efficiently in the image collecting process. (3) The correction of collected images was tested. The sum of the Euclidean distance between the uncorrected color and the standard color was 1296.345, whereas the sum between the corrected color and the standard color was 403.527. The maximum distances before and after correction were 163.68 and 44.69, respectively. The minimum distances before and after correction were 13.3 and 5.9, respectively. (4) We collected 4050 photos of patients using this system, which was proven to be stable. Conclusions: This article introduces an automated acquisition system of facial diagnosis in TCM. The safety of the system can be ensured. By comparing TCM under natural light and in the dark box, this system meets the requirements of clinical application in all of the collected samples (more than 4050). The acquisition system of facial diagnosis in TCM has also been applied efficiently in a few hospitals.

scholarly journals Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method

2020 ◽  
Vol 10 (11) ◽  
pp. 3832 ◽  
Author(s):  
Sung Wook Kim ◽  
Young Gon Lee ◽  
Bayu Adhi Tama ◽  
Seungchul Lee
Keyword(s):  
Large Scale ◽  
Binary Classification ◽  
Predictive Performance ◽  
Threshold Value ◽  
Regression Method ◽  
Scale Production ◽  
Modulation Transfer ◽  
Camera Lens ◽  
Large Scale Production ◽  
Training Samples

Artificial intelligence has become the primary issue in the era of Industry 4.0, accelerating the realization of a self-driven smart factory. It is transforming various manufacturing sectors including the assembly line for a camera lens module. The recent development of bezel-less smartphones necessitates a large-scale production of the camera lens module. However, assembling the necessary parts of a module needs much room to be improved since the procedure followed by its inspection is costly and time-consuming. Consequently, the collection of labeled data is often limited. In this study, a reliable means to predict the state of an unseen camera lens module using simple semi-supervised regression is proposed. Here, an experimental study to investigate the effect of different numbers of training samples is demonstrated. The increased amount of data using simple pseudo-labeling means is shown to improve the general performance of deep neural network for the prediction of Modulation Transfer Function (MTF) by as much as 18%, 15% and 25% in terms of RMSE, MAE and R squared. The cross-validation technique is used to ensure a generalized predictive performance. Furthermore, binary classification is conducted based on a threshold value for MTF to finally demonstrate the better prediction outcome in a real-world scenario. As a result, the overall accuracy, recall, specificity and f1-score are increased by 11.3%, 9%, 1.6% and 7.6% showing that the classification of camera lens module has been improved through the suggested semi-supervised regression method.

scholarly journals ANALYSIS OF UAV IMAGE QUALITY USING EDGE ANALYSIS

2018 ◽  
Vol XLII-4 ◽  
pp. 359-364
Author(s):  
P.-C. Lim ◽  
T. Kim ◽  
S.-I. Na ◽  
K.-D. Lee ◽  
H.-Y. Ahn ◽  
...  
Keyword(s):  
Image Quality ◽  
Spatial Resolution ◽  
Rating Scales ◽  
Signal To Noise Ratio ◽  
Weather Conditions ◽  
Resolving Power ◽  
Modulation Transfer ◽  
Image Sharpness ◽  
Actual Performance ◽  
Uav Images

<p><strong>Abstract.</strong> UAVs (Unmanned aerial Vehicles) can acquire images easily without large cost. For this reason, use of UAV is spreading to diverse fields such as orthoimages and DEM/DSM production. The spatial resolution of images is usually expressed as a GSD (Ground Sampling Distance). The GSD from UAV has higher performance than other platforms such as satellites and aircraft because it shoot at low altitude. However, blurring and noise may occur on UAV images due to the weather and the stability of UAV. However, since the GSD from UAV cannot sufficiently meet the spatial resolving power of the actual image system, a criterion for determining the spatial resolution of image is needed. Therefore we emphasize that the quality of the image needs to be analysed. Actual performance indicators such as GRD (Ground Resolved Distance) and NIIRS (National Image Interpretability Rating Scales), which can be measured through image analysis, are representative examples of image quality interpretation. It is possible to extract NIIRS form image quality related parameters such as MTF (Modulation Transfer Function), RER (Relative Edge Response) and SNR (Signal to Noise Ratio). In this paper, we aim to apply the Edge analysis method to UAV and to analyse the result. The analysis result showed that while GSD and NIIRS were highly dependent to imaging altitude, GRD and image sharpness showed optimal altitude ranges. The exact optimal range varied between images taken at different weather conditions. While we need a further study, this may indicate that edge analysis may provide an optimal operational altitude range suitable for the sensors.</p>

scholarly journals Artifact Reduction in Compressed Sensing Averaging Techniques for High-Resolution Magnetic Resonance Images

2021 ◽  
Vol 11 (21) ◽  
pp. 9802
Author(s):  
Jeong-Min Shim ◽  
Young-Bo Kim ◽  
Chang-Ki Kang
Keyword(s):  
Compressed Sensing ◽  
High Frequency ◽  
Transfer Functions ◽  
Magnetic Resonance Images ◽  
Acquisition Time ◽  
Modulation Transfer ◽  
Image Sharpness ◽  
Spatiotemporal Resolution ◽  
Ringing Artifacts ◽  
Ringing Artifact

This study aims to introduce a new compressed sensing averaging (CSA) technique for the reduction of blurring and/or ringing artifacts, depending on the k-space sampling ratio. A full k-space dataset and three randomly undersampled datasets were obtained for CSA images in a brain phantom and a healthy subject. An additional simulation was performed to assess the effect of the undersampling ratio on the images and the signal-to-noise ratios (SNRs). The image sharpness, spatial resolution, and contrast between tissues were analyzed and compared with other CSA techniques. Compared to CSA with multiple acquisition (CSAM) at 25%, 35%, and 45% undersampling, the reduction rates of the k-space lines of CSA with keyhole (CSAK) were 10%, 15%, and 22%, respectively, and the acquisition time was reduced by 16%, 23%, and 32%, respectively. In the simulation performed with a full sampling k-space dataset, the SNR decreased to 10.41, 9.80, and 8.86 in the white matter and 9.69, 9.35, and 8.46 in the gray matter, respectively. In addition, the ringing artifacts became substantially more predominant as the number of sampling lines decreased. The 50% modulation transfer functions were 0.38, 0.43, and 0.54 line pairs per millimeter for CSAM, CSAK with high-frequency sharing (CSAKS), and CSAK with high-frequency copying (CSAKC), respectively. In this study, we demonstrated that the smaller the sampling line, the more severe the ringing artifact, and that the CSAKC technique proposed to overcome the artifacts that occur when using CSA techniques did not generate artifacts, while it increased spatiotemporal resolution.

scholarly journals Impact of Patient Alignment on Image Quality in C-Arm Computed Tomography – Evaluation Using an ACR Phantom

2020 ◽  
Author(s):  
Babak Alikhani ◽  
Julius Renne ◽  
Sabine Maschke ◽  
Jan B. Hinrichs ◽  
Frank K. Wacker ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Signal To Noise Ratio ◽  
Image Acquisition ◽  
Dose Intensity ◽  
Noise Signal ◽  
Image Noise ◽  
Modulation Transfer ◽  
Low Contrast ◽  
Heel Effect

Purpose To evaluate the influence of patient alignment and thereby heel effect on the image quality (IQ) of C-arm flat-panel detector computed tomography (CACT). Materials and Methods An ACR phantom placed in opposite directions along the z-axis (setup A and B) on the patient support was imaged using CACT. Image acquisition was performed with three different image acquisition protocols. The images were reconstructed with four convolution kernels. IQ was assessed in terms of high contrast using the modulation transfer function (MTF) and low contrast by assessing the image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratios (CNR) as well as the reliability of density measurements. Furthermore, the dose intensity profiles were measured free-in-air. Results The MTF in setup B is higher than the MTF measured in setup A (p < 0.01). The image noises measured in setup A for the air and bone inserts were higher compared to those measured in setup B (p > 0.05). Opposite behavior has been observed for the polyethylene, water-equivalent and acrylic inserts. The SNR for all inserts is inversely related to the image noise. A systematically increasing or decreasing trend of CNR could not be observed (p > 0.05). The intensity profile measured by the detector system free-in-air showed that the anode heel effect is perpendicular to the z-axis. Conclusion The patient alignment has a minor influence on the IQ of CACT. This effect is not based on the X-ray anode heel effect but is caused mainly by the non-symmetrical rotation of CACT. Key Points:  Citation Format

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