Image Quality Measures Research Articles

Subjective Quality Assessment of V-PCC-Compressed Dynamic Point Clouds Degraded by Packet Losses.

This article describes an empirical exploration on the effect of information loss affecting compressed representations of dynamic point clouds on the subjective quality of the reconstructed point clouds. The study involved compressing a set of test dynamic point clouds using the MPEG V-PCC (Video-based Point Cloud Compression) codec at 5 different levels of compression and applying simulated packet losses with three packet loss rates (0.5%, 1% and 2%) to the V-PCC sub-bitstreams prior to decoding and reconstructing the dynamic point clouds. The recovered dynamic point clouds qualities were then assessed by human observers in experiments conducted at two research laboratories in Croatia and Portugal, to collect MOS (Mean Opinion Score) values. These scores were subject to a set of statistical analyses to measure the degree of correlation of the data from the two laboratories, as well as the degree of correlation between the MOS values and a selection of objective quality measures, while taking into account compression level and packet loss rates. The subjective quality measures considered, all of the full-reference type, included point cloud specific measures, as well as others adapted from image and video quality measures. In the case of image-based quality measures, FSIM (Feature Similarity index), MSE (Mean Squared Error), and SSIM (Structural Similarity index) yielded the highest correlation with subjective scores in both laboratories, while PCQM (Point Cloud Quality Metric) showed the highest correlation among all point cloud-specific objective measures. The study showed that even 0.5% packet loss rates reduce the decoded point clouds subjective quality by more than 1 to 1.5 MOS scale units, pointing out the need to adequately protect the bitstreams against losses. The results also showed that the degradations in V-PCC occupancy and geometry sub-bitstreams have significantly higher (negative) impact on decoded point cloud subjective quality than degradations of the attribute sub-bitstream.

Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics.

In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoration, reconstruction, translation, as well as objective image quality assessment. Despite the impressive progress in generating high-resolution, perceptually realistic images, it is not clear if modern GANs reliably learn the statistics that are meaningful to a downstream medical imaging application. In this work, the ability of a state-of-the-art GAN to learn the statistics of canonical stochastic image models (SIMs) that are relevant to objective assessment of image quality is investigated. It is shown that although the employed GAN successfully learned several basic first- and second-order statistics of the specific medical SIMs under consideration and generated images with high perceptual quality, it failed to correctly learn several per-image statistics pertinent to the these SIMs, highlighting the urgent need to assess medical image GANs in terms of objective measures of image quality.

Underwater Attentional Generative Adversarial Networks for Image Enhancement

In this article, to exclusively suppress unuseful underwater noise feature and effectively avoid overenhancement, simultaneously, an underwater attentional generative adversarial network (UAGAN) is innovatively established. Main contributions are as follows: combining dense concatenation with global maximum and average pooling techniques, a cascade dense-channel attention (CDCA) module is devised to adaptively distinguish noise feature and recalibrate channel weight, simultaneously, such that low-contribution feature map can be effectively suppressed; to sufficiently capture long-range dependence between any two nonlocal spatial patches, the position attention (PA) module is created such that the deviation among independent patches can be sufficiently eliminated, thereby avoiding overenhancement; and in conjunction with CDCA and PA modules, the entire UAGAN framework is eventually developed in an end-to-end manner. Comprehensive experiments conducted on underwater image enhancement benchmark (UIEB) and underwater robot professional contest (URPC) datasets demonstrate remarkable effectiveness and superiority of the proposed UAGAN scheme by comparing with typical underwater image enhancement approaches including unsupervised color correction method, image blurriness and light absorption, underwater dark channel prior, underwater generative adversarial network, underwater convolutional neural network, and WaterNet in terms of peak signal-to-noise ratio, underwater color image quality evaluation, underwater image quality measures, etc.

Feasibility of accelerated non-contrast-enhanced whole-heart bSSFP coronary MR angiography by deep learning-constrained compressed sensing.

To examine a compressed sensing artificial intelligence (CSAI) framework to accelerate image acquisition in non-contrast-enhanced whole-heart bSSFP coronary magnetic resonance (MR) angiography. Thirty healthy volunteers and 20 patients with suspected coronary artery disease (CAD) scheduled for coronary computed tomography angiography (CCTA) were enrolled. Non-contrast-enhanced coronary MR angiography was performed with CSAI, compressed sensing (CS), and sensitivity encoding (SENSE) methods in healthy participants and with CSAI in patients. Acquisition time, subjective image quality score, and objective image quality measurement (blood pool homogeneity, signal-to-noise ratio [SNR], and contrast-to-noise ratio [CNR]) were compared among the three protocols. The diagnostic performance of CASI coronary MR angiography for predicting significant stenosis (≥ 50% diameter stenosis) on CCTA was evaluated. The Friedman test was performed to compare the three protocols. Acquisition time was significantly shorter in the CSAI and CS groups than in the SENSE group (10.2 ± 3.2min vs. 10.9 ± 2.9min vs. 13.0 ± 4.1min, p < 0.001). However, the CSAI approach had the highest image quality scores, blood pool homogeneity, mean SNR value, and mean CNR value (all p < 0.001) compared with the CS and SENSE approaches. The sensitivity, specificity, and accuracy of CSAI coronary MR angiography per patient were 87.5% (7/8), 91.7% (11/12), and 90.0% (18/20); those per vessel were 81.8% (9/11), 93.9% (46/49), and 91.7% (55/60); and those per segment were 84.6% (11/13), 98.0% (244/249), and 97.3% (255/262), respectively. CSAI yielded superior image quality within a clinically feasible acquisition time in healthy participants and patients with suspected CAD. The non-invasive and radiation-free CSAI framework could be a promising tool for rapid screening and comprehensive examination of the coronary vasculature in patients with suspected CAD. • This prospective study showed that CSAI enables a reduction in acquisition time by 22% with superior diagnostic image quality compared with the SENSE protocol. • CSAI replaces the wavelet transform with a CNN as a sparsifying transform in the CS algorithm, achieving high coronary MR image quality with reduced noise. • CSAI achieved per-patient sensitivity of 87.5% (7/8) and specificity of 91.7% (11/12) respectively for detecting significant coronary stenosis.

The Development of a Cost-Effective Imaging Device Based on Thermographic Technology

Thermal vision-based devices are nowadays used in a number of industries, ranging from the automotive industry, surveillance, navigation, fire detection, and rescue missions to precision agriculture. This work describes the development of a low-cost imaging device based on thermographic technology. The proposed device uses a miniature microbolometer module, a 32-bit ARM microcontroller, and a high-accuracy ambient temperature sensor. The developed device is capable of enhancing RAW high dynamic thermal readings obtained from the sensor using a computationally efficient image enhancement algorithm and presenting its visual result on the integrated OLED display. The choice of microcontroller, rather than the alternative System on Chip (SoC), offers almost instantaneous power uptime and extremely low power consumption while providing real-time imaging of an environment. The implemented image enhancement algorithm employs the modified histogram equalization, where the ambient temperature sensor helps the algorithm enhance both background objects near ambient temperature and foreground objects (humans, animals, and other heat sources) that actively emit heat. The proposed imaging device was evaluated on a number of environmental scenarios using standard no-reference image quality measures and comparisons against the existing state-of-the-art enhancement algorithms. Qualitative results obtained from the survey of 11 subjects are also provided. The quantitative evaluations show that, on average, images acquired by the developed camera provide better perception quality in 75% of tested cases. According to qualitative evaluations, images acquired by the developed camera provide better perception quality in 69% of tested cases. The obtained results verify the usability of the developed low-cost device for a range of applications where thermal imaging is needed.

The Analysis of Underwater Imagery System for Armor Unit Monitoring Application

The placement of armor units for breakwaters in Indonesia is still done manually, which depends on divers in each placement of the armor unit. The use of divers is less effective due to limited communication between divers and excavator operators, making divers in the water take a long time. This makes the diver's job risky and expensive. This research presents a vision system to reduce the diver's role in adjusting the position of each armor unit. This vision system is built with two cameras connected to a mini-computer. This system has an image improvement process by comparing three methods. The results obtained are an average frame per second is 20.71 without applying the method, 0.45 fps for using the multi-scale retinex with color restoration method, 16.75 fps for applying the Contrast Limited Adaptive Histogram Equalization method, 16.17 fps for applying the Histogram Equalization method. The image quality evaluation uses the underwater color quality evaluation with 48 data points. The method that has experienced the most improvement in image quality is multi-scale retinex with color restoration. Forty data have improved image quality with an average of 14,131, or 83.33%. The number of images that experienced the highest image quality improvement was using the multi-scale retinex with color restoration method. Meanwhile, for image quality analysis based on Underwater Image Quality Measures, out of a total of 48 images, the method with the highest value for image quality is the contrast limited adaptive histogram equalization method. 100% of images have the highest image matrix value with an average value is 33.014.

Impact of uptake time on image quality of [68 Ga]Ga-PSMA-11 PET/CT.

With the introduction of prostate specific membrane antigen (PSMA) PET/CT, the detection rate of prostate cancer metastases has improved significantly, both for primary staging and for biochemical recurrence. EANM/SNMMI guidelines recommend a 60min time interval between [68 Ga]Ga-PSMA administration and acquisition. This study evaluates the possibility of a shorter time interval by investigating the dynamic change in image quality measures. We retrospectively analyzed 10 consecutive prostate cancer patients who underwent a dynamic whole body [68 Ga]Ga-PSMA-11 PET/CT of 75min from skull vertex to mid-thigh using Siemens FlowMotion. PET images were acquired directly after injection of 1.5MBq/kg [68 Ga]Ga-PSMA-11. Image quality measures included lesion maximum standardized uptake value corrected for lean body mass (SULmax ), tumor-to-background ratio (TBR), and contrast-to-noise ratio (CNR). Quantitative analysis of image quality in dynamic PET was performed using PMOD (version 4.2). Regions of interest (ROIs), drawn included different types of prostate lesions (primary tumor, lymph nodes, and bone metastasis), organ tissue (liver, spleen, lacrimal gland, submandibular gland, parotid gland, urinary bladder, kidneys blood pool [ascending aorta], left ventricle), bone tissue (4th lumbar vertebral body [L4]) and muscle tissue (gluteus maximus). To further investigate image quality four 10min multi-frame reconstructions with clinical parameters were made at different post-injection times (15, 30, 45, and 60min). A nuclear medicine physician performed a blinded lesion detectability evaluation on these multi-frame reconstructions for different prostate cancer lesions. Six primary prostate tumors in seven patients with prostate in situ, 13 lymph node metastases in six patients and up to 12 bone metastases in three patients were found. The different prostate lesion types (lymph nodes metastases, bone metastases, and primary prostate tumor) all show an increase in average SULmax , TBR, and CNR over time during the scan. The normalized average SULmax , TBR, and CNR of the combined prostate lesions at 15, 30, and 45min post-injection scans were all significant p<0.05 lower from the 60min post-injection [68 Ga]Ga-PSMA-11 PET/CT (9.5±4.5, 12.7±6.2, and 41.8±24.5, respectively). At patient level, the reader concluded the same regarding the presence/absence of primary prostate cancer recurrence, lymph node metastases, and/or bone metastases on all <60min post-injection [68 Ga]Ga-PSMA-11 PET/CT's in comparison to the reference scan (60min post-injection). At lesion level, all bone metastases seen on the reference scan were also seen on all <60min post-injection [68 Ga]Ga-PSMA-11 PET/CT's but there were some lymph nodes (n=2) metastases missed on the 15, 30, and 45min post-injection scans. One lymph node metastasis on both the 15 and 30min post-injection [68 Ga]Ga-PSMA-11 PET/CT's was missed and one lymph node metastasis was missed, only on the 45min post-injection [68 Ga]Ga-PSMA-11 PET/CT. Shorter post-injection times (15, 30, and 45min) compared to the recommended post-injection time of 60min are not optimal. However, the impact of a shorter time interval of 45min instead of 60min between [68 Ga]Ga-PSMA-11 administration and the start of PET/CT acquisition on both image quality (SULmax , TBR, and CNR) and lesion detection, while significant, is small.

Normal Values of Three-Dimensional Right Ventricular Size and Function Measurements: Results of the World Alliance Societies of Echocardiography Study

Normal values for 3D right ventricular (RV) size and function are not well established, as they originate from small studies that involved predominantly white North American and European populations, did not use RV-focused views and relied on older 3D RV analysis software . The World Alliance of Societies of Echocardiography (WASE) study was designed to generate reference ranges for normal subjects around the world. In this study, we sought to assess the world-wide capability of 3D imaging of the right ventricle and report size and function measurements, including their dependency on age, sex and ethnicity. Healthy subjects free of cardiac, pulmonary and renal disease were prospectively enrolled at 19 centers in 15 countries, including 6 continents. 3D wide-angle RV datasets were obtained and analyzed using dedicated RV software (Tomtec) to measure end-diastolic and end-systolic volumes (EDV, ESV), stroke volume (SV) and ejection fraction (EF). Results were categorized by sex, age (18-40, 41-65 and >65 years) and ethnicity. Of the 2007 subjects with attempted 3D RV acquisitions, 1051 had adequate image quality for confident measurements. Upper and lower limits for BSA-indexed EDV (mL/m2) and ESV (mL/m2) and EF (%) were [48, 95], [19, 43] and [44, 58] for men and [42, 81], [16, 36] and [46, 61] for women. Men had significantly larger EDV, ESV and SV (even after BSA indexing) and lower EF than women (p<0.05). EDV and ESV did not show any meaningful differences between age groups. 3D RV volumes were smallest in Asians. Reliability of 3D RV acquisition is low worldwide underscoring the importance for future improvements in imaging techniques. Sex and race must be taken into consideration in the assessment of both RV volumes and EF.

A Non-Convex Hybrid Overlapping Group Sparsity Model with Hyper-Laplacian Prior for Multiplicative Noise

Multiplicative noise removal is a quite challenging problem in image denoising. In recent years, hyper-Laplacian prior information has been successfully introduced in the image denoising problem and significant denoising effects have been achieved. In this paper, we propose a new hybrid regularizer model for removing multiplicative noise. The proposed model consists of the non-convex higher-order total variation and overlapping group sparsity on a hyper-Laplacian prior regularizer. It combines the advantages of the non-convex regularization and the hybrid regularization, which may simultaneously preserve the fine-edge information and reduce the staircase effect at the same time. We develop an effective alternating minimization method for the proposed nonconvex model via an alternating direction method of multipliers framework, where the majorization–minimization algorithm and the iteratively reweighted algorithm are adopted to solve the corresponding subproblems. Numerical experiments show that the proposed model outperforms the most advanced model in terms of visual quality and certain image quality measurements.

Acquisition time reduction in pediatric 99mTc‐DMSA planar imaging using deep learning

Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric 99m Tc-dimercaptosuccinic acid (DMSA) scintigraphy. The aim of this study was to evaluate the performance of predicted full-acquisition-time images from short-acquisition-time pediatric 99m Tc-DMSA planar images with only 1/5th acquisition time using deep learning in terms of image quality and quantitative renal uptake measurement accuracy. One hundred and fifty-five cases that underwent pediatric 99m Tc-DMSA planar imaging as dynamic data for 10min were retrospectively collected for the development of three deep learning models (DnCNN, Win5RB, and ResUnet), and the generation of full-time images from short-time images. We used the normalized mean squared error (NMSE), peak signal-to-noise ratio (PSNR), and structural similarity index metrics (SSIM) to evaluate the accuracy of the predicted full-time images. In addition, the renal uptake of 99m Tc-DMSA was calculated, and the difference in renal uptake from the reference full-time images was assessed using scatter plots with Pearson correlation and Bland-Altman plots. The predicted full-time images from the deep learning models showed a significant improvement in image quality compared to the short-time images with respect to the reference full-time images. In particular, the predicted full-time images obtained by ResUnet showed the lowest NMSE (0.4 [0.4-0.5] %) and the highest PSNR (55.4 [54.7-56.1] dB) and SSIM (0.997 [0.995-0.997]). For renal uptake, an extremely high correlation was achieved in all short-time and three predicted full-time images (R2 >0.999 for all). The Bland-Altman plots showed the lowest bias (-0.10) of renal uptake in ResUnet, while short-time images showed the lowest variance (95% confidence interval: -0.14, 0.45) of renal uptake. Our proposed method is capable of producing images that are comparable to the original full-acquisition-time images, allowing for a reduction of acquisition time/injected dose in pediatric 99m Tc-DMSA planar imaging.

A Quantitative Analysis of Smartphone-Based Endoscopy and Video Tower Endoscopy.

Examine the differences between traditional tower-based endoscopy (TBE) and smartphone-based endoscopy (SBE) using objective measures of cost, setup time, and image quality. Cost analysis study and randomized single-blinded prospective trial was performed at a tertiary academic health center. Twenty-three healthcare providers, 2 PA-C, 9 residents, 2 fellows, 10 attendings varying in practice from 1 to 27 years were a part of the study. Actual cost analysis was used for purchase of the Karl Storz video tower system and the Save My Scope smartphone-based endoscopy system for cost analysis. For setup time, providers entered a room and were randomized to set up either an SBE or TBE system and timed from room entry to a visible on-screen image. A crossover was then performed so all providers performed both setups. For image discernment, standardized photos of a modified Snellen's test were sent via text message to providers who were blinded as to which photo represented which system. Practitioners were randomized as to which photo to receive first. Cost savings was 95.8% ($39,917 USD) per system. Setup time for the smartphone system was 46.7 seconds less than video tower system on average (61.5 vs 23.5 seconds; P < .001, 95% CI: 30.3-63.1 seconds). Level of visual discernment was slightly better for SBE over TBE, with reviewers able to identify Snellen test letters at a size of 4.2 mm with SBE versus 5.9 mm with TBE (P < .001). Smartphone-based endoscopy was found to be cheaper, quicker to set up, and to have marginally better image quality when transmitted via messaging than tower-based endoscopy, although the clinical significance of these visual differences are unknown. If appropriate for their needs, clinicians should consider smartphone-based endoscopy as a viable option for viewing and collaborating on endoscopic images from a fiberoptic endoscope.

Convolutional neural network-based apple images classification and image quality measurement by light colors using the color-balancing approach

Convolutional neural network-based apple images classification and image quality measurement by light colors using the color-balancing approach

Fringe pattern analysis to evaluate light sources and sensors in digital photoelasticity.

When experimental photoelasticity images are acquired, the spectral interaction between the light source and the sensor used affect the visual information of the fringe patterns in the produced images. Such interaction can lead to fringe patterns with an overall high quality, but also can lead to images with indistinguishable fringes, and bad stress field reconstruction. We introduce a strategy to evaluate such interaction that relies on measuring the value of four handcrafted descriptors: contrast, an image descriptor that accounts simultaneously for blur and noise, a Fourier-based descriptor to measure image quality, and image entropy. The utility of the proposed strategy was validated by measuring the selected descriptors on computational photoelasticity images, and the fringe orders achieved when evaluating the stress field, from 240 spectral configurations: 24 light sources and 10 sensors. We found that high values of the selected descriptors can be related to spectral configurations that lead to better stress field reconstruction. Overall, the results show that the selected descriptors can be useful to identify bad and good spectral interactions, which could help to design better protocols for acquiring photoelasticity images.

Recovering Microscopic Images in Material Science Documents by Image Inpainting

Microscopic images in material science documents have increased in number due to the growth and common use of electron microscopy instruments. Through the use of data mining techniques, they are easily accessible and can be obtained from documents published online. As data-driven approaches are becoming increasingly common in the material science field, massively acquired experimental images through microscopy play important roles in terms of developing an artificial intelligence (AI) model for the purposes of automatically diagnosing crucial material structures. However, irrelevant objects (e.g., letters, scale bars, and arrows) that are often present inside original microscopic photos should be removed for the purposes of improving the AI models. To avoid the issue above, we applied four image inpainting algorithms (i.e., shift-net, global and local, contextual attention, and gated convolution) to a learning approach, with the aim of recovering microscopic images in journal papers. We estimated the structural similarity index measure (SSIM) and ℓ1/ℓ2 errors, which are often used as measures of image quality. Lastly, we observed that gated convolution possessed the best performance for inpainting the microscopic images.

A new approach for super-resolution and classification applications on neonatal thermal images

ABSTRACT Thermal imaging systems are harmless to human health and enable contactless heat measurements. The thermal cameras are used in many public sectors where it is necessary to detect the change of temperature values. However, thermal cameras are costly and produce images with low edge information. This situation prevents the widespread use of thermal cameras. Therefore, in recent years, the researches to advance the quality of thermal images have increased. Within the scope of the studies in this paper, first of all, three different datasets consisting of thermal images in the colourful format of neonates were created. Also, TSRGAN+ deep network model was presented for super-resolution studies. The super-resolution images obtained visually approached ground truth images to a great extent. In addition, these results were compared using the peak signal to noise ratio (PSNR) and the structural similarity index measure (SSIM) image quality metrics. The proposed model showed a superior success in terms of the values of PSNR and SSIM compared to the state-of-the-art models. Here, the PSNR value of the proposed TSRGAN+ model increased by 1–1.5 dB compared to the TSRGAN network architecture, while the SSIM value increased by around 2–3%. Finally, the unhealthy-healthy image classification applications were performed on all thermal image sets in order to implement both the task-based evaluation and a real-life application. Thus, a new method is presented to evaluate the results of the super-resolution studies. Here, firstly, a CNN-based classifier was designed and the classification metrics were obtained for all three datasets. Then, transfer learning was applied using state-of-the-art models (ResNet101, Xception) to increase classification success. Here, the most successful results were obtained in applications using the ResNet101 model. Also, the developed model outperformed the TSRGAN, which achieved the second-best result. When all the obtained results are evaluated, it has been observed that the super-resolution models increase the success of unhealthy-healthy classification by about 10% compared to the low resolution images. In other words, the effects of super-resolution techniques on classification applications are clearly seen. In summary, the logical use of the super-resolution research will enable the common use of low-cost thermal cameras in the application fields such as medicine.

Linearized Analysis of Noise and Resolution for DL-Based Image Generation.

Deep-learning (DL) based CT image generation methods are often evaluated using RMSE and SSIM. By contrast, conventional model-based image reconstruction (MBIR) methods are often evaluated using image properties such as resolution, noise, bias. Calculating such image properties requires time consuming Monte Carlo (MC) simulations. For MBIR, linearized analysis using first order Taylor expansion has been developed to characterize noise and resolution without MC simulations. This inspired us to investigate if linearization can be applied to DL networks to enable efficient characterization of resolution and noise. We used FBPConvNet as an example DL network and performed extensive numerical evaluations, including both computer simulations and real CT data. Our results showed that network linearization works well under normal exposure settings. For such applications, linearization can characterize image noise and resolutions without running MC simulations. We provide with this work the computational tools to implement network linearization. The efficiency and ease of implementation of network linearization can hopefully popularize the physics-related image quality measures for DL applications. Our methodology is general; it allows flexible compositions of DL nonlinear modules and linear operators such as filtered-backprojection (FBP). For the latter, we develop a generic method for computing the covariance images that is needed for network linearization.

Hexagonal-Grid-Layout Image Segmentation Using Shock Filters: Computational Complexity Case Study for Microarray Image Analysis Related to Machine Learning Approaches

Hexagonal grid layouts are advantageous in microarray technology; however, hexagonal grids appear in many fields, especially given the rise of new nanostructures and metamaterials, leading to the need for image analysis on such structures. This work proposes a shock-filter-based approach driven by mathematical morphology for the segmentation of image objects disposed in a hexagonal grid. The original image is decomposed into a pair of rectangular grids, such that their superposition generates the initial image. Within each rectangular grid, the shock-filters are once again used to confine the foreground information for each image object into an area of interest. The proposed methodology was successfully applied for microarray spot segmentation, whereas its character of generality is underlined by the segmentation results obtained for two other types of hexagonal grid layouts. Considering the segmentation accuracy through specific quality measures for microarray images, such as the mean absolute error and the coefficient of variation, high correlations of our computed spot intensity features with the annotated reference values were found, indicating the reliability of the proposed approach. Moreover, taking into account that the shock-filter PDE formalism is targeting the one-dimensional luminance profile function, the computational complexity to determine the grid is minimized. The order of growth for the computational complexity of our approach is at least one order of magnitude lower when compared with state-of-the-art microarray segmentation approaches, ranging from classical to machine learning ones.

A Novel Method to Model Image Creation Based on Mammographic Sensors Performance Parameters: A Theoretical Study.

Mammographic digital imaging is based on X-ray sensors with solid image quality characteristics. These primarily include (a) a response curve that yields high contrast and image latitude, (b) a frequency response given by the Modulation Transfer Function (MTF), which enables small detail imaging and (c) the Normalize Noise Power Spectrum (NNPS) that shows the extent of the noise effect on image clarity. In this work, a methodological approach is introduced and described for creating digital phantom images based on the measured image quality properties of the sensor. For this purpose, a mathematical phantom, simulating breast tissue and lesions of blood, adipose, muscle, Ca and Ca(50%)-P(50%) was created by considering the corresponding X-ray attenuation coefficients. The simulated irradiation conditions of the phantom used four mammographic spectra assuming exponential attenuation. Published data regarding noise and blur of a commercial RadEye HR CMOS imaging sensor were used as input data for the resulting images. It was found that the Ca and Ca(50%)-P(50%) lesions were visible in all exposure conditions. In addition, the W/Rh spectrum at 28 kVp provided more detailed images than the corresponding Mo/Mo spectrum. The presented methodology can act complementarily to image quality measurements, leading to initial optimization of the X-ray exposure parameters per clinical condition.

Improved Image Quality Assessment by Utilizing Pre-Trained Architecture Features with Unified Learning Mechanism

The purpose of the no-reference image quality assessment (NR-IQA) is to measure perceived image quality based on subjective judgments; however, due to the lack of a clean reference image, this is a complicated and unresolved challenge. Massive new IQA datasets have facilitated the creation of deep learning-based image quality measurements. We present a unique model to handle the NR-IQA challenge in this research by employing a hybrid strategy that leverages from pre-trained CNN model and the unified learning mechanism that extracts both local and non-local characteristics from the input patch. The deep analysis of the proposed framework shows that the model uses features and a mechanism that improves the monotonicity relationship between objective and subjective ratings. The intermediary goal was mapped to a quality score using a regression architecture. To extract various feature maps, a deep architecture with an adaptive receptive field was used. Analyses of this biggest NR-IQA benchmark datasets demonstrate that the suggested technique outperforms current state-of-the-art NR-IQA measures.

Review: A Survey on Objective Evaluation of Image Sharpness

Establishing an accurate objective evaluation metric of image sharpness is crucial for image analysis, recognition and quality measurement. In this review, we highlight recent advances in no-reference image quality assessment research, divide the reported algorithms into four groups (spatial domain-based methods, spectral domain-based methods, learning-based methods and combination methods) and outline the advantages and disadvantages of each method group. Furthermore, we conduct a brief bibliometric study with which to provide an overview of the current trends from 2013 to 2021 and compare the performance of representative algorithms on public datasets. Finally, we describe the shortcomings and future challenges in the current studies.