- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Predicting detection performance on security X-ray images as a function of image quality
摘要： Developing methods to predict how image quality affects task performance is a topic of great interest in many applications. While such studies have been performed in the medical imaging community, little work has been reported in the security X-ray imaging literature. In this work, we develop models that predict the effect of image quality on the detection of improvised explosive device (IED) components by bomb technicians in images taken using portable X-ray systems. Using a newly developed NIST-LIVE X-Ray Task Performance Database, we created a set of objective algorithms that predict bomb technician detection performance based on measures of image quality. Our basic measures are traditional Image Quality Indicators (IQIs) and perceptually-relevant Natural Scene Statistics (NSS)-based measures that have been extensively used in visible light (VL) image quality prediction algorithms. We show that these measures are able to quantify the perceptual severity of degradations and can predict the performance of expert bomb technicians to identify threats. Combining NSS- and IQI-based measures yields even better task performance prediction than either of these methods independently. We also developed a new suite of statistical task prediction models that we refer to as Quality Inspectors of X-ray images (QUIX), which we believe to be the first NSS-based model for security X-ray images. We also show that QUIX can be used to reliably predict conventional IQI metric values on distorted X-ray images.
关键词： NSS,IQI prediction,IEEE/ANSI N42.55,Image Quality,Improvised explosive devices (IEDs),Task performance study,X-ray images
更新于2025-09-19 17:15:36
-
[IEEE 2019 Photonics North (PN) - Quebec City, QC, Canada (2019.5.21-2019.5.23)] 2019 Photonics North (PN) - Efficient hot-band pumped Nd:YLF laser
摘要： Most publicly available image quality databases have been created under highly controlled conditions by introducing graded simulated distortions onto high-quality photographs. However, images captured using typical real-world mobile camera devices are usually afflicted by complex mixtures of multiple distortions, which are not necessarily well-modeled by the synthetic distortions found in existing databases. The originators of existing legacy databases usually conducted human psychometric studies to obtain statistically meaningful sets of human opinion scores on images in a stringently controlled visual environment, resulting in small data collections relative to other kinds of image analysis databases. Toward overcoming these limitations, we designed and created a new database that we call the LIVE In the Wild Image Quality Challenge Database, which contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices. We also designed and implemented a new online crowdsourcing system, which we have used to conduct a very large-scale, multi-month image quality assessment (IQA) subjective study. Our database consists of over 350 000 opinion scores on 1162 images evaluated by over 8100 unique human observers. Despite the lack of control over the experimental environments of the numerous study participants, we demonstrate excellent internal consistency of the subjective data set. We also evaluate several top-performing blind IQA algorithms on it and present insights on how the mixtures of distortions challenge both end users as well as automatic perceptual quality prediction models. The new database is available for public use at http://live.ece.utexas.edu/research/ChallengeDB/index.html.
关键词： crowdsourcing,subjective image quality assessment,authentic distortions,Perceptual image quality
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Spatially Resolved Material Quality Prediction Via Constrained Deep Learning
摘要： Positron emission tomography (PET) images are typically reconstructed with an in-plane pixel size of approximately 4 mm for cancer imaging. The objective of this work was to evaluate the effect of using smaller pixels on general oncologic lesion-detection. A series of observer studies was performed using experimental phantom data from the Utah PET Lesion Detection Database, which modeled whole-body FDG PET cancer imaging of a 92 kg patient. The data comprised 24 scans over 4 days on a Biograph mCT time-of-flight (TOF) PET/CT scanner, with up to 23 lesions (diam. 6–16 mm) distributed throughout the phantom each day. Images were reconstructed with 2.036 mm and 4.073 mm pixels using ordered-subsets expectation-maximization (OSEM) both with and without point spread function (PSF) modeling and TOF. Detection performance was assessed using the channelized non-prewhitened numerical observer with localization receiver operating characteristic (LROC) analysis. Tumor localization performance and the area under the LROC curve were then analyzed as functions of the pixel size. In all cases, the images with ～2 mm pixels provided higher detection performance than those with ～4 mm pixels. The degree of improvement from the smaller pixels was larger than that offered by PSF modeling for these data, and provided roughly half the benefit of using TOF. Key results were confirmed by two human observers, who read subsets of the test data. This study suggests that a significant improvement in tumor detection performance for PET can be attained by using smaller voxel sizes than commonly used at many centers. The primary drawback is a 4-fold increase in reconstruction time and data storage requirements.
关键词： PET/CT reconstruction,PET/CT,image reconstruction,Image quality assessment
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Strong voltage-boost effect in two-step photon-up conversion solar cells
摘要： Single-image super resolution is a process of obtaining a high-resolution image from a set of low-resolution observations by signal processing. While super resolution has been demonstrated to improve image quality in scaled down images in the image domain, its effects on the Fourier-based image acquisition technique, such as MRI, remains unknown. We performed high-resolution ex vivo late gadolinium enhancement (LGE) magnetic resonance imaging (0.4 × 0.4 × 0.4 mm3) in postinfarction swine hearts (n = 24). The swine hearts were divided into the training set (n = 14) and the test set (n = 10), and in all hearts, low-resolution images were simulated from the high-resolution images. In the training set, super-resolution dictionaries with pairs of small matching patches of the high- and low-resolution images were created. In the test set, super resolution recovered high-resolution images from low-resolution images using the dictionaries. The same algorithm was also applied to patient LGE (n = 4) to assess its effects. Compared with interpolated images, super resolution significantly improved basic image quality indices (P < 0.001). Super resolution using Fourier-based zero padding achieved the best image quality. However, the magnitude of improvement was small in images with zero padding. Super resolution substantially improved the spatial resolution of the patient LGE images by sharpening the edges of the heart and the scar. In conclusion, single-image super resolution significantly improves image errors. However, the magnitude of improvement was relatively small in images with Fourier-based zero padding. These findings provide evidence to support its potential use in myocardial scar imaging.
关键词： magnetic resonance imaging,Image processing,image quality,myocardial scar
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Photovoltaic Inverter Momentary Cessation: Recovery Process is Key
摘要： Positron emission tomography (PET) images are typically reconstructed with an in-plane pixel size of approximately 4 mm for cancer imaging. The objective of this work was to evaluate the effect of using smaller pixels on general oncologic lesion-detection. A series of observer studies was performed using experimental phantom data from the Utah PET Lesion Detection Database, which modeled whole-body FDG PET cancer imaging of a 92 kg patient. The data comprised 24 scans over 4 days on a Biograph mCT time-of-flight (TOF) PET/CT scanner, with up to 23 lesions (diam. 6–16 mm) distributed throughout the phantom each day. Images were reconstructed with 2.036 mm and 4.073 mm pixels using ordered-subsets expectation-maximization (OSEM) both with and without point spread function (PSF) modeling and TOF. Detection performance was assessed using the channelized non-prewhitened numerical observer with localization receiver operating characteristic (LROC) analysis. Tumor localization performance and the area under the LROC curve were then analyzed as functions of the pixel size. In all cases, the images with ～2 mm pixels provided higher detection performance than those with ～4 mm pixels. The degree of improvement from the smaller pixels was larger than that offered by PSF modeling for these data, and provided roughly half the benefit of using TOF. Key results were confirmed by two human observers, who read subsets of the test data. This study suggests that a significant improvement in tumor detection performance for PET can be attained by using smaller voxel sizes than commonly used at many centers. The primary drawback is a 4-fold increase in reconstruction time and data storage requirements.
关键词： Image quality assessment,PET/CT reconstruction,PET/CT,image reconstruction
更新于2025-09-19 17:13:59
-
[IEEE 2019 16th International Conference on the European Energy Market (EEM) - Ljubljana, Slovenia (2019.9.18-2019.9.20)] 2019 16th International Conference on the European Energy Market (EEM) - Shapley-Value-Based Distribution of the Costs of Solar Photovoltaic Plant Grid Connection
摘要： Positron emission tomography (PET) images are typically reconstructed with an in-plane pixel size of approximately 4 mm for cancer imaging. The objective of this work was to evaluate the effect of using smaller pixels on general oncologic lesion-detection. A series of observer studies was performed using experimental phantom data from the Utah PET Lesion Detection Database, which modeled whole-body FDG PET cancer imaging of a 92 kg patient. The data comprised 24 scans over 4 days on a Biograph mCT time-of-flight (TOF) PET/CT scanner, with up to 23 lesions (diam. 6–16 mm) distributed throughout the phantom each day. Images were reconstructed with 2.036 mm and 4.073 mm pixels using ordered-subsets expectation-maximization (OSEM) both with and without point spread function (PSF) modeling and TOF. Detection performance was assessed using the channelized non-prewhitened numerical observer with localization receiver operating characteristic (LROC) analysis. Tumor localization performance and the area under the LROC curve were then analyzed as functions of the pixel size. In all cases, the images with ～2 mm pixels provided higher detection performance than those with ～4 mm pixels. The degree of improvement from the smaller pixels was larger than that offered by PSF modeling for these data, and provided roughly half the benefit of using TOF. Key results were confirmed by two human observers, who read subsets of the test data. This study suggests that a significant improvement in tumor detection performance for PET can be attained by using smaller voxel sizes than commonly used at many centers. The primary drawback is a 4-fold increase in reconstruction time and data storage requirements.
关键词： PET/CT reconstruction,PET/CT,image reconstruction,Image quality assessment
更新于2025-09-16 10:30:52
-
Image quality enhanced recognition of laser cavity based on improved random hough transform
摘要： The tedious measurement of arc parts with low accuracy is serious problem in the traditional industrial measurement. In this paper, the method for the image quality enhanced recognition with digital image processing technology is developed by changing the direct detection of arc parts of the laser cavity into the detection of contour curve in the image. A circular arc fitting algorithm based on the improved random hough transform (RHT) is proposed to improve the disadvantages of RHT such as strong noise disturbance, high requirements for the extraction of contour continuity and slow calculation speed. The differences between the distance from the center of the fitting circle to all points in the testing area and the fitting radius were calculated. The minimum value was obtained to determine the optimal fitting circular arc. The algorithm is tested and applied to detect the actual workpiece. It is demonstrated that the accuracy and speed of cavity detection are much better by comparision with the traditional algorithm by the proposed improved algorithm.
关键词： Optimal circular arc fitting algorithm,Arc parts detection,Improved random hough transform,Image quality enhanced recognition
更新于2025-09-12 10:27:22
-
Generating Image Distortion Maps Using Convolutional Autoencoders with Application to No Reference Image Quality Assessment
摘要： We present two contributions in this work: (i) a reference-free image distortion map generating algorithm for spatially localizing distortions in a natural scene, and (ii) no reference image quality assessment (NRIQA) algorithms derived from the generated distortion map. We use a convolutional autoencoder (CAE) for distortion map generation. We rely on distortion maps generated by the SSIM image quality assessment (IQA) algorithm as the “ground truth” for training the CAE. We train the CAE on a synthetically generated dataset composed of pristine images and their distorted versions. Specifically, the dataset was created by applying standard distortions such as JPEG compression, JP2K compression, Additive White Gaussian Noise (AWGN) and blur to the pristine images. SSIM maps are then generated on a per distorted image basis for each of the distorted images in the dataset and are in turn used for training the CAE. We first qualitatively demonstrate the robustness of the proposed distortion map generation algorithm over several images with both traditional and authentic distortions. We also demonstrate the distortion map’s effectiveness quantitatively on both standard distortions and authentic distortions by deriving three different NRIQA algorithms. We show that these NRIQA algorithms deliver competitive performance over traditional databases like LIVE Phase II, CSIQ, TID 2013, LIVE MD and MDID 2013, and databases with authentic distortions like LIVE Wild and KonIQ-10K. In summary, the proposed method generates high quality distortion map that are used to design robust NRIQA algorithms. Further, the CAE based distortion maps generation method can easily be modified to work with other ground truth distortion maps.
关键词： Convolutional neural network,no reference image quality assessment (IQA),human visual system (HVS),autoencoders
更新于2025-09-11 14:15:04
-
[IEEE 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX) - Cagliari (2018.5.29-2018.6.1)] 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX) - Subjective Assessment of Post-Processing Methods for Low Light Consumer Photos
摘要： Consumer photos taken in low light conditions often suffer from substantial undesired capture artifacts, such as shakiness and sensor noise. In this paper, we use rank ordering method to assess the subjective preferences among different post-processing methods used to alleviate capture artifacts. The results show that most users prefer sharpened photos, even in the presence of substantial sensor noise. However, there are also systematic differences in individual preferences between users. Therefore, user preferences need to be considered in addition to the image characteristics, when selecting the post-processing algorithms and parameters for photo quality enhancement.
关键词： image quality,capture artifacts,rank ordering,subjective quality assessment
更新于2025-09-10 09:29:36
-
[IEEE 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI) - Shanghai, China (2018.9.6-2018.9.7)] 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI) - No-reference image quality assessment based on dualchannel convolutional neural network
摘要： In recent years, convolutional neural networks have achieved more outstanding results and been widely used in the field of image quality assessment compared with the traditional handcraft method. This paper presents a no-reference image quality assessment method based on dual-channel convolutional neural network. The raw image is labeled by visual information fidelity and divided into multiple patches as input. After that, feature extraction is performed by two network channels with different pooling layers. The features are linearly stitched and sent to the fully connected layer. The experimental results on the LIVE database and the TID2008 database show that our model has the state-of-the-art performance and obtain a better consistency with human subjective assessment.
关键词： convolutional neural network,image quality assessment,dual-channel
更新于2025-09-10 09:29:36