scholarly journals Image Motion Measurement and Image Restoration System Based on an Inertial Reference Laser

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3309
Author(s):  
Ronggang Yue ◽  
Humei Wang ◽  
Ting Jin ◽  
Yuting Gao ◽  
Xiaofeng Sun ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Restoration ◽  
Motion Detection ◽  
Vibration Isolation ◽  
Image Motion ◽  
Integrating Sphere ◽  
Modulation Transfer ◽  
Motion Measurement ◽  
Motion Data ◽  
Reference Laser

Satellites have many high-, medium-, and low-frequency micro vibration sources that lead to the optical axis jitter of the optical load and subsequently degrade the remote sensing image quality. To address this problem, this paper developed an image motion detection and restoration method based on an inertial reference laser, and describe edits principle and key components. To verify the feasibility and performance of this method, this paper also built an image motion measurement and restoration system based on an inertial reference laser, which comprised a camera (including the inertial reference laser unit and a Hartmann wavefront sensor), an integrating sphere, a simulated image target, a parallel light pope, a vibration isolation platform, a vibration generator, and a 6 degrees of freedom platform. The image restoration principle was also described. The background noise in the experiment environment was measured, and an image motion measurement accuracy experiment was performed. Verification experiments of image restoration were also conducted under various working conditions. The experiment results showed that the error of image motion detection based on the inertial reference laser was less than 0.12 pixels (root mean square). By using image motion data to improve image quality, the modulation transfer function (MTF) of the restored image was increased to 1.61–1.88 times that of the original image MTF. The image motion data could be used as feedback to the fast steering mirror to compensate for the satellite jitter in real time and to directly obtain high-quality images.

ANALISIS RESTORASI CITRA KABUR ALGORITMA WIENER MENGGUNAKAN INDEKS KUALITAS CITRA

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Nur Afiyat
Keyword(s):  
Image Quality ◽  
Image Restoration ◽  
Quality Index ◽  
Input Image ◽  
Motion Blur ◽  
Image Motion ◽  
Original Image ◽  
Degraded Image ◽  
Gaussian Blur ◽  
Image Quality Index

Degradation and additional noise in an image will make the quality decreases. Image restoration is needed to restore the image quality to be similar to the original state. Damage to the image can restored include: blurred image, the image with noise spots, dual image, over-saturated color, and the pixel error. To make theblur image is modeled as a convolution between the original image with the point spread function (PSF) which is a point or object spectrum will be spread out so that objects appear to fade. Image restoration is done by passing a blurry image on a filter. In this study discussed Wiener image restoration algorithm using the input image is degraded motion blur and Gaussian blur. Quality image restoration results were analyzed using the image quality index, by comparing the image of the restoration of the original image as a reference. Further image restoration results used as the input image is then processed using Index Image Analysis GUI application. Each of the input image must have a resolution and dimensions that are identical to a reference image. The results showed that by providing opaqueness different models on the same image, the degree of blurring that occurs will be different. Image quality index results for the restoration of degraded image higher than the Gaussian blur image of the restoration of degraded image motion blur. Image quality index results for the restoration of degraded image motion blur ranged from 0.84229 up to 0.87146. Image quality index results for the restoration of degraded Gaussian blur images ranging from 0.86969 up to 0.90025.Keywords: Restoration, blur image, PSF, Wiener algorithm, the image quality index.

Effect of Image Motion and Vibration on Image Quality of TDICCD Camera

2011 ◽  
Vol 128-129 ◽  
pp. 584-588
Author(s):  
Zhi Le Wang ◽  
Xu Xia Zhuang ◽  
Lan Qing Zhang
Keyword(s):  
Image Quality ◽  
Transfer Function ◽  
Phase Transfer ◽  
Linear Part ◽  
Low Frequency ◽  
Image Motion ◽  
Geometric Distortion ◽  
Modulation Transfer ◽  
Image Blur

Image motion and vibration are significant factors influencing image quality of satellite-based TDI camera. In order to learn their effect on TDICCD, motion degradation models at two directions were established. Based on the models, Line spread functions (LSF) for image motion of different types and strict expressions of MTF for abnormal linear image motion were deduced. Modulation transfer function (MTF) for sinusoidal image vibration was calculated through numerical integration. Geometric distortion was evaluated by linear part of phase transfer function (PTF). The results indicate that abnormal linear motion and high frequency vibration cause space-invariant image blur while low frequency vibration brings space-variant image blur and geometric distortion.

scholarly journals Influence of patient motion on quantitative accuracy in cardiac 15O-water positron emission tomography

2021 ◽  
Author(s):  
Jonny Nordström ◽  
Hendrik J. Harms ◽  
Tanja Kero ◽  
Jens Sörensen ◽  
Mark Lubberink
Keyword(s):  
Motion Detection ◽  
Detection Algorithm ◽  
Clinical Effects ◽  
Average Deviation ◽  
Patient Motion ◽  
Motion Data ◽  
Positron Emission ◽  
Quantitative Accuracy ◽  
Pet Ct ◽  
Using Data

Abstract Background Patient motion is a common problem during cardiac PET. The purpose of the present study was to investigate to what extent motions influence the quantitative accuracy of cardiac 15O-water PET/CT and to develop a method for automated motion detection. Method Frequency and magnitude of motion was assessed visually using data from 50 clinical 15O-water PET/CT scans. Simulations of 4 types of motions with amplitude of 5 to 20 mm were performed based on data from 10 scans. An automated motion detection algorithm was evaluated on clinical and simulated motion data. MBF and PTF of all simulated scans were compared to the original scan used as reference. Results Patient motion was detected in 68% of clinical cases by visual inspection. All observed motions were small with amplitudes less than half the LV wall thickness. A clear pattern of motion influence was seen in the simulations with a decrease of myocardial blood flow (MBF) in the region of myocardium to where the motion was directed. The perfusable tissue fraction (PTF) trended in the opposite direction. Global absolute average deviation of MBF was 3.1% ± 1.8% and 7.3% ± 6.3% for motions with maximum amplitudes of 5 and 20 mm, respectively. Automated motion detection showed a sensitivity of 90% for simulated motions ≥ 10 mm but struggled with the smaller (≤ 5 mm) simulated (sensitivity 45%) and clinical motions (accuracy 48%). Conclusion Patient motion can impair the quantitative accuracy of MBF. However, at typically occurring levels of patient motion, effects are similar to or only slightly larger than inter-observer variability, and downstream clinical effects are likely negligible.

scholarly journals Evaluation of DISORDER: retrospective image motion correction for volumetric brain MRI in a pediatric setting

2020 ◽  
Author(s):  
Katy Vecchiato ◽  
Alexia Egloff ◽  
Olivia Carney ◽  
Ata Siddiqui ◽  
Emer Hughes ◽  
...  
Keyword(s):  
Image Quality ◽  
Motion Correction ◽  
Spin Echo ◽  
Brain Mri ◽  
Quality Metrics ◽  
Image Motion ◽  
Reference Image ◽  
Outlier Rejection ◽  
Image Quality Metrics ◽  
Correction Technique

Background and Purpose: Head motion causes image degradation in brain MRI examinations, negatively impacting image quality, especially in pediatric populations. Here, we used a retrospective motion correction technique in children and assessed image quality improvement for 3D MRI acquisitions. Material and Methods: We prospectively acquired brain MRI at 3T using 3D sequences, T1-weighted MPRAGE, T2-weighted Turbo Spin Echo and FLAIR, in 32 unsedated children, including 7 with epilepsy (age range 2-18 years). We implemented a novel motion correction technique: Distributed and Incoherent Sample Orders for Reconstruction Deblurring using Encoding Redundancy (DISORDER). For each subject and modality, we obtained 3 reconstructions: as acquired (Aq), after DISORDER motion correction (Di), and Di with additional outlier rejection (DiOut). We analyzed 288 images quantitatively, measuring 2 objective no-reference image quality metrics: Gradient Entropy (GE) and MPRAGE White Matter Homogeneity (WM-H). As a qualitative metric, we presented blinded and randomized images to 2 expert neuroradiologists who scored them for clinical readability. Results: Both image quality metrics improved after motion correction for all modalities and improvement correlated with the amount of intrascan motion. Neuroradiologists also considered the motion corrected images as of higher quality (Wilcoxon z=-3.164 MPRAGE, z=-2.066 TSE, z=-2.645 FLAIR, for all p<0.05). Conclusions: Retrospective image motion correction with DISORDER increased image quality both from an objective and qualitative perspective. In 75% of sessions, at least one sequence was improved by this approach, indicating the benefit of this technique in un-sedated children for both clinical and research environments.

Image Quality and Target Recognition

1967 ◽  
Vol 9 (1) ◽  
pp. 5-32 ◽  
Author(s):  
Corwin A. Bennett ◽  
Samuel H. Winterstein ◽  
Robert E. Kent
Keyword(s):  
Grain Size ◽  
Image Quality ◽  
Modulation Transfer Function ◽  
Target Recognition ◽  
Recognition Performance ◽  
Aerial Photographs ◽  
Modulation Transfer ◽  
Task Definition ◽  
Subject Selection ◽  
Target Characteristics

The terminology and literature in the area of image quality and target recognition are reviewed. An experiment in which subjects recognized strategic and tactical targets in aerial photographs with controlled image degradations is described. Some findings are: Recognition performance is only moderate for representative conditions. There are wide differences among target types in the recognizability. Knowledge of a target's presence (briefing) greatly aids recognition. Better resolution means better performance. Enlarging the image such that a line of resolution subtends more than three minutes of arc hinders recognition. Grain size should be kept below 20 seconds of arc. It is suggested that the eventual application of the modulation transfer function approach to measurement of image quality and target characteristics will enable a quantitative subsuming of various quality-size relationships. More attention needs to be paid in recognition research to suitable task definition, target description, and subject selection.

Research on Multiplicity of Solution in Space Camera Image Motion Measurement Based on Images from TDI Sensor Overlapped Area

2014 ◽  
Vol 34 (12) ◽  
pp. 1212002
Author(s):  
刘海秋 Liu Haiqiu ◽  
王栋 Wang Dong ◽  
徐抒岩 Xu Shuyan
Keyword(s):  
Image Motion ◽  
Motion Measurement ◽  
Camera Image

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

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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