- 标题
- 摘要
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- 实验方案
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No-reference image quality assessment using gradient magnitude and wiener filtered wavelet features
摘要： No-reference image quality assessment (NR-IQA) aims to evaluate the perceived quality of distorted images without prior knowledge of pristine version of the images. The quality score is predicted based on the features extracted from the distorted image, which needs to correlate with the mean opinion score. The prediction of an image quality score becomes a trivial task, if the noise affecting the quality of an image can be modeled. In this paper, gradient magnitude and Wiener filtered discrete wavelet coefficients are utilized for image quality assessment. In order to reconstruct an estimated noise image, Wiener filter is applied to discrete wavelet coefficients. The estimated noise image and the gradient magnitude are modeled as conditional Gaussian random variables. Joint adaptive normalization is applied to the conditional random distribution of the estimated noise image and the gradient magnitude to form a feature vector. The feature vector is used as an input to a pre-trained support vector regression model to predict the image quality score. The proposed NR-IQA is tested on five commonly used image quality assessment databases and shows better performance as compared to the existing NR-IQA techniques. The experimental results show that the proposed technique is robust and has good generalization ability. Moreover, it also shows good performance when training is performed on images from one database and testing is performed on images from another database.
关键词： Wiener filtering,Gradient magnitude,Discrete wavelet transform,No-reference image quality assessment
更新于2025-09-23 15:21:21
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Improvement of image quality at CT and MRI using deep learning
摘要： Deep learning has been developed by computer scientists. Here, we discuss techniques for improving the image quality of diagnostic computed tomography and magnetic resonance imaging with the aid of deep learning. We categorize the techniques for improving the image quality as “noise and artifact reduction”, “super resolution” and “image acquisition and reconstruction”. For each category, we present and outline the features of some studies.
关键词： Computed tomography,Image quality improvement,Deep learning,Magnetic resonance imaging
更新于2025-09-23 15:21:01
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[IEEE 2018 2nd IEEE Advanced Information Management,Communicates, Electronic and Automation Control Conference (IMCEC) - Xi'an (2018.5.25-2018.5.27)] 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC) - Studying the Effect of ROI on Image Quality Using ERPS
摘要： The effect of region of interest (ROI) on the overall image quality assessment was studied using subjective rating method and objective psychophysiology measurement methods (e.g., event-related potentials (ERPs) technology. Different image quality was acquired by compressing only the ROI or background area with different σ values of Gaussian Kernel Function. Also, the amplitude and latency of P300 (ERPs component at about 300-550ms) changed with different compressed types. The ROI compressed stimuli had earlier and higher P300 component than that of NROI compressed stimuli. The results showed that it was easier to detect the difference between original image and impaired image of ROI compressed stimuli than that of NROI compressed stimuli.
关键词： event-related potentials,region of interest,image quality
更新于2025-09-23 15:21:01
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Image quality assessment method based on relation intensity and details similarity
摘要： In this paper, we present an e?ective quality assessment method based on the relation intensity ratio and detail similarity for image quality assessment (IQA) with the full reference image, which ?rst allows us to compute the nonlinear gradient magnitude with Gaussian smoothing of the reference and distorted images and construct the relation intensity ratio and detail similarity between them. Next, the ?nal IQA map is formed by linearly combining the relation intensity ratio with the detail similarity. Finally, we adopt a new pooling strategy which e?ectively integrates the mean and standard deviation of the ?nal IQA map to accurately predict image quality. Experiments based on two publicly available databases show that the proposed method can provide accurate predictions compared with most state-of-the-art IQA methods.
关键词： detail similarity,relation intensity ratio,Image quality assessment
更新于2025-09-23 15:21:01
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[Advances in Intelligent Systems and Computing] Image Processing and Communications Challenges 10 Volume 892 (10th International Conference, IP&C’2018 Bydgoszcz, Poland, November 2018, Proceedings) || Reliability of Local Ground Truth Data for Image Quality Metric Assessment
摘要： Image Quality Metrics (IQMs) automatically detect di?erences between images. For example, they can be used to ?nd aliasing artifact in the computer generated images. An obvious application is to test if the costly anti-aliasing techniques must be applied so that the aliasing is not visible to humans. The performance of IQMs must be tested based on the ground truth data, which is a set of maps that indicate the location of artifacts in the image. These maps are manually created by people during so called marking experiments. In this work, we evaluate two di?erent techniques of marking. In the side-by-side experiment, people mark di?erences between two images displayed side-by-side on the screen. In the ?ickering experiment, images are displayed at the same location but are exchanged over time. We assess the performance of each technique and use the generated reference maps to evaluate the performance of the selected IQMs. The results reveal the better accuracy of the ?ickering technique.
关键词： Image quality metrics,Aliasing,Ground truth data,Perceptual experiments
更新于2025-09-23 15:21:01
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2D and 3D Image Quality Assessment: A Survey of Metrics and Challenges
摘要： Image quality is important not only for the viewing experience, but also for the performance of image processing algorithms. Image quality assessment (IQA) has been a topic of intense research in the fields of image processing and computer vision. In this paper, we first analyze the factors that affect two-dimensional (2D) and three-dimensional (3D) image quality, and then provide an up-to-date overview on IQA for each main factor. The main factors that affect 2D image quality are fidelity and aesthetics. Another main factor that affects stereoscopic 3D image quality is visual comfort. We also describe the IQA databases and give the experimental results on representative IQA metrics. Finally, we discuss the challenges for IQA, including the influence of different factors on each other, the performance of IQA metrics in real applications, and the combination of quality assessment, restoration, and enhancement.
关键词： image aesthetics assessment,visual comfort,Image quality assessment,and image quality enhancement.
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Addressing Safety Issues in Development of Quantum Dot Incorporated EVA Lamination of Photovoltaic Devices
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2025-09-23 15:19:57
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Fibrous Al-Doped ZnO Thin Film Ultraviolet Photodetectors with Improved Responsivity and Speed
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2025-09-23 15:19:57
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Optimization of cone beam computed tomography image quality in implant dentistry
摘要： This study was conducted to optimize the cone beam computed tomography image quality in implant dentistry using both clinical and quantitative image quality evaluation with measurement of the radiation dose. A natural bone human skull phantom and an image quality phantom were used to evaluate the images produced after changing the exposure parameters (kVp and mA). A 10 × 5 cm2 field of view was selected for average adult. Five scans were taken with varying kVp (70–90 kVp) first at fixed 4 mA. After assessment of the scans and selecting the best kVp, nine scans were taken with 2–12 mA, and the kVp was fixed at the optimal value. A clinical assessment of the implant‐related anatomical landmarks was done in random order by two blinded examiners. Quantitative image quality was assessed for noise/uniformity, artifact added value, contrast‐to‐noise ratio, spatial resolution, and geometrical distortion. A dosimetry index phantom and thimble ion chamber were used to measure the absorbed dose for each scan setting. The anatomical landmarks of the maxilla had good image quality at all kVp settings. To produce good quality images, the mandibular landmarks demanded higher exposure parameters than the maxillary landmarks. The quantitative image quality values were acceptable at all selected exposure settings. Changing the exposure parameters does not necessarily produce higher image quality outcomes but does affect the radiation dose to the patient. The image quality could be optimized for implant treatment planning at lower exposure settings and dose than the default settings.
关键词： radiation dose,image quality,clinical image evaluation,dental CBCT,implant treatment planning
更新于2025-09-23 15:19:57
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Multi-Pooled Inception Features for No-Reference Image Quality Assessment
摘要： Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attaching global average pooling (GAP) layers to multiple Inception modules of on an ImageNet database pretrained convolutional neural network (CNN). In contrast to previous methods, we do not take patches from the input image. Instead, the input image is treated as a whole and is run through a pretrained CNN body to extract resolution-independent, multi-level deep features. As a consequence, our method can be easily generalized to any input image size and pretrained CNNs. Thus, we present a detailed parameter study with respect to the CNN base architectures and the effectiveness of different deep features. We demonstrate that our best proposal—called MultiGAP-NRIQA—is able to outperform the state-of-the-art on three benchmark IQA databases. Furthermore, these results were also confirmed in a cross database test using the LIVE In the Wild Image Quality Challenge database.
关键词： deep learning,no-reference image quality assessment,convolutional neural networks
更新于2025-09-23 15:19:57