Perceived Image Quality Research Articles

Non-orthogonal spectacle correction for irregular astigmatism.

To investigate the potential improvement in visual acuity and subjective perception of image quality in patients with keratoconus using non-orthogonal correction (NOC) cylinder trial lenses where the steep and flat power meridians are set at angles less or greater than 90°. A set of NOC plano/cylindrical trial lenses, where the axes between the power meridians were set at a range of non-orthogonal angles, were used to refract 18 participants with keratoconus in whom 23 eyes were used for testing. Corneal elevation data were processed by bespoke MATLAB code from Pentacam Scheimpflug tomographer scans. Each participant first underwent subjective refraction using standard orthogonal cylinder trial lenses, and the monocular best-corrected visual acuity (BCVA, logMAR) was recorded for each eye. They then underwent a second subjective refraction using NOC cylinder trial lenses created for the study and completed a questionnaire to elicit their subjective appraisal of letter clarity and ghosting. Fourteen (61%) eyes demonstrated an increase in objective BCVA with the NOC versus the orthogonal correction; seven (30%) eyes showed no change and in two (9%) eyes, the BCVA was slightly worse. Further, 87% and 79% experienced an increase in letter clarity and a reduction in ghosting, respectively, independent of changes in BCVA. The majority of non-orthogonal angles were in the range of 80°-85°, and it was possible to refine the cylinder and axis of the NOC further compared with the orthogonal correction. All but one of the participants said they would be interested in trying non-orthogonal spectacles if the opportunity arose. Correcting irregular astigmatism in keratoconic individuals with non-orthogonal spectacle correction may provide benefit in terms of increased visual acuity, improvements in letter clarity and a reduction of ghosting effects. This type of correction has the potential to improve the overall quality of life for patients with keratoconus.

SRARDA: A lightweight adaptive residual dense attention generative adversarial network for image super-resolution

Image super-resolution (SR) is the task of inferring a high resolution (HR) image from one/multiple single low resolution (LR) input(s). Traditional networks are evaluated by pixel-level metrics such as Peak-Signal-to-Noise Ratio (PSNR) etc., which do not always align with human perception of image quality. They often produce excessively smooth images that lack high-frequency texture and appear unnatural. Therefore, in this paper, we propose a lightweight adaptive residual dense attention generative adversarial network (SRARDA) for image SR. Firstly, our generator adopts the residual in residual (RIR) structure but redesigns the basic module. By using dynamic residual connection (ARC) to dynamically adjust the importance of residual and main paths, we design a novel adaptive residual dense attention block (ARDAB) that enhances the feature extraction capability of the generator. In addition, we build a high-frequency filtering unit (HFU) to extract more high-frequency features from the LR space. Finally, to fully utilize the discriminator, we use WGAN to compute the difference between the HR image and the reconstructed image. Experiments demonstrate that SRARDA effectively addresses the issue of excessive smoothing in reconstructed images, while also enhancing visual quality.

Low-dose computed tomography perceptual image quality assessment

In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural similarity index measure, are often employed as alternatives. However, these metrics, initially developed for natural images, may not fully encapsulate the radiologists’ assessment process. Consequently, interest in developing deep learning-based image quality assessment (IQA) methods that more closely align with radiologists’ perceptions is growing. A significant barrier to this development has been the absence of open-source datasets and benchmark models specific to CT IQA. Addressing these challenges, we organized the Low-dose Computed Tomography Perceptual Image Quality Assessment Challenge in conjunction with the Medical Image Computing and Computer Assisted Intervention 2023. This event introduced the first open-source CT IQA dataset, consisting of 1,000 CT images of various quality, annotated with radiologists’ assessment scores. As a benchmark, this challenge offers a comprehensive analysis of six submitted methods, providing valuable insight into their performance. This paper presents a summary of these methods and insights. This challenge underscores the potential for developing no-reference IQA methods that could exceed the capabilities of full-reference IQA methods, making a significant contribution to the research community with this novel dataset. The dataset is accessible at https://zenodo.org/records/7833096.

A paradigm shift in oncology imaging: a prospective cross-sectional study to assess low-dose deep learning image reconstruction versus standard-dose iterative reconstruction for comprehensive lesion detection in dual-energy computed tomography.

Low-kiloelectron volt (keV) virtual monochromatic images (VMIs) from low-dose (LD) dual-energy computed tomography (DECT) can enhance lesion contrast but suffer from high image noise. Recently, a deep learning image reconstruction (DLIR) algorithm has been developed and shown significant potential in suppressing image noise and improving image quality. To date, the capacity of LD low-keV thoracic-abdominal-pelvic DECT with DLIR to detect various types of tumor lesions have not been assessed. Hence, this study aimed to evaluate the image quality and lesion detection capabilities of LD VMIs using DLIR with thoracic-abdominal-pelvic DECT versus standard-dose (SD) iterative reconstruction (IR) in oncology patients. This prospective intraindividual study included 56 oncology patients who received a SD (13.86 mGy) and a consecutive LD (7.15 mGy) thoracic-abdominal-pelvic DECT from April 2022 to July 2023 at The First Affiliated hospital of Zhengzhou University. SD VMIs were reconstructed using IR at 50 keV (SD-IR50 keV), while LD VMIs were processed using DLIR at 50 keV (LD-DL50 keV) and 40 keV (LD-DL40 keV), respectively. Quantitative image parameters [computed tomography (CT) values, image noise, and contrast-to-noise ratios (CNRs)], qualitative metrics (image noise, vessel conspicuity, image contrast, artificial sensation, and overall image quality), and lesion CNRs and conspicuity were compared. The lesion detection rates in the SD-IR50 keV, LD-DL50 keV, and LD-DL40 keV VMIs were assessed according to lesion location (lung, liver, and lymph), type, and size. Repeated measures analysis of variance and the Friedman test were applied for comparing quantitative and qualitative measures, respectively. The Cochran Q test was used for comparing lesion detection rates. Compared to SD-IR50 keV VMIs, LD-DL50 keV VMIs showed similar CT values and image noise (P>0.05), similar (P>0.05) or higher(P<0.05) CNRs, similar (P>0.05) or superior (P<0.05) perceptual image quality, and similar (P>0.05) or higher (P<0.001) lesion CNR and conspicuity. LD-DL40 keV VMIs exhibited higher CT values (by 40.4-47.1%) and CNRs (by 21.8-39.8%) (P<0.001), equivalent image noise, similar (P>0.05) or superior (P<0.05) perceptual image quality except for artificial sensation, and similar (P>0.05) or higher (P<0.001) lesion CNRs (by 16.5-46.3%) and conspicuity. The VMIs of LD-DL50 keV and LD-DL40 keV were consistent with those of SD-IR50 keV in terms of lesion detection capability in pulmonary nodules [SD-IR50 keV vs. LD-DL50 keV vs. LD-DL40 keV: 88/88 (100.0%) vs. 88/88 (100.0%) vs. 88/88 (100.0%); P>0.99], for lymph nodes [125/126 (99.2%) vs. 123/126 (97.6%) vs. 124/126 (98.4%); P>0.05], and high-contrast liver lesions [12/12 (100.0%) vs. 12/12 (100.0%) vs. 12/12 (100.0%); P>0.05], but not for small liver lesions (≤0.5 cm) [63/65 (96.9%) vs. 43/65 (66.2%) vs. 51/65 (78.5%); P<0.05] or low-contrast liver lesions [198/200 (99.0%) vs. 174/200 (87.0%) vs. 183/200 (91.5%); P<0.05]. VMIs at 40 keV with DLIR enables a 50% decrease in the radiation dose while largely maintaining diagnostic capabilities for multidetection of pulmonary nodules, lymph nodes, and liver lesions in oncology patients.

ConIQA: A deep learning method for perceptual image quality assessment with limited data

Effectively assessing the realism and naturalness of images in virtual (VR) and augmented (AR) reality applications requires Full Reference Image Quality Assessment (FR-IQA) metrics that closely align with human perception. Deep learning-based IQAs that are trained on human-labeled data have recently shown promise in generic computer vision tasks. However, their performance decreases in applications where perfect matches between the reference and the distorted images should not be expected, or whenever distortion patterns are restricted to specific domains. Tackling this issue necessitates training a task-specific neural network, yet generating human-labeled FR-IQAs is costly, and deep learning typically demands substantial labeled data. To address these challenges, we developed ConIQA, a deep learning-based IQA that leverages consistency training and a novel data augmentation method to learn from both labeled and unlabeled data. This makes ConIQA well-suited for contexts with scarce labeled data. To validate ConIQA, we considered the example application of Computer-Generated Holography (CGH) where specific artifacts such as ringing, speckle, and quantization errors routinely occur, yet are not explicitly accounted for by existing IQAs. We developed a new dataset, HQA1k, that comprises 1000 natural images each paired with an image rendered using various popular CGH algorithms, and quality-rated by thirteen human participants. Our results show that ConIQA achieves superior Pearson (0.98), Spearman (0.965), and Kendall’s tau (0.86) correlations over fifteen FR-IQA metrics by up to 5%, showcasing significant improvements in aligning with human perception on the HQA1k dataset.

RAD-IQMRI: A benchmark for MRI image quality assessment

Magnetic resonance imaging (MRI) is susceptible to visual artifacts that can degrade the perceptual image quality, potentially leading to inaccurate or inefficient diagnoses in clinical practice. It is critical to evaluate the perceptual image quality and build this technique into clinical solutions. In a previous study, an MRI database was created for image quality assessment (IQA), where various types of MRI artifacts with different degrees of degradation were simulated. Application specialists assessed the image quality; however, radiologists’ perception of MRI image quality remains unknown. To make IQA clinically relevant, in this paper we conduct a new subjective experiment where 13 radiologists rated the quality of images contained in the MRI database. Based on this subjective IQA benchmark named RAD-IQMRI, we evaluate the performance of state-of-the-art objective IQA models, providing insights into their application for MRI image quality assessment in clinical settings.

Comparison of the perceived image quality of intraoral orthodontic photographs taken with DSLR camera and mobile phone camera: A double-blinded prospective study

Clinical orthodontic photography is a vital skill that every orthodontist should master to record the patients’ details and to permit the orthodontist to carefully plan, monitor and execute the treatment. With the advancement of technology, some clinicians opt to take intraoral photographs with their mobile phone rather than DSLR camera. Hence, this study aimed to answer one main question: whether there was any significant difference in the perceived quality image between intraoral photos taken with a mobile phone and a standard DSLR camera. The cameras used were a DSLR (Nikon D300s with AF-S Micro NIKKOR 105mm lens and NIKON R1C1 Twin Flash) and Mobile Phone (Apple I-Phone 11 with Selfie Ring Light). Assessment of 20 sets of intraoral photographs (100 individual images) by five IIUM orthodontists using a perceived quality Likert scale of Zero (0) to Ten (10). The assessors and the lead investigator were blinded to the source of the photographs. Reliability was evaluated using a test-retest method on 4 sets of intraoral photographs (20 individual images), a few weeks after their initial assessment. There was no significant difference (p=0.35) in perceived quality of intraoral photographs taken between DSLR and mobile phone, with the mean value of 7.34 and 7.12 respectively. Reliability was good (ICC=0.549). This prospective study showed that there was no statistical difference between the perceived quality of intraoral orthodontic photographs taken with a DSLR camera and a Mobile Phone camera.

GR-GAN: A unified adversarial framework for single image glare removal and denoising

In this paper, we introduce the single image glare removal (SIGR) task. SIGR aims to eliminate glare or light caused by external environment in lighting scene. To efficiently improve natural image quality and usability, many research tasks, such as image deraining and shadow removal, have been investigated a lot. However, SIGR is still underexplored. Therefore, we propose to construct a dataset and explore deep learning-based models for SIGR. Our contributions can be summarized as follows: (1) We establish a new benchmark dataset for SIGR, termed De-Glare, aiming to propel research of SIGR. This dataset comprises pairs of {glare, glare-free} images sourced from both real-world and synthetic data, utilized for training and evaluating models. (2) We conduct a comprehensive benchmarking of extensive state-of-the-art (SoTA) methods on the constructed De-Glare dataset and provide insightful analyses based on the results. (3) An innovative approach for single image glare removal employing a multi-scale generative adversarial network (GR-GAN) model is proposed. Glare images typically exhibit irregular glare shapes and cluttered backgrounds. To address these irregular glare patterns, we introduce a deformable convolution-based glare attention detector (GAD) designed to generate an attention map which specifics glare spots or rays in the input image. In pursuit of enhancing the perceptual quality of output image, GR-GAN adaptively filters out irrelevant noises and enhances salient features through a generator with cascaded pyramid neck (CPN) network. This work can provide useful insights for developing better SIGR models. Without specific tuning, our method achieves the SoTA results on multiple computer vision tasks, including the image deraining and image shadow removal.

Generic color correction for tone mapping operators in high dynamic range imaging

One of the significant challenges in tone mapping is to preserve the perceptual quality of the tone-mapped images. Traditional tone mapping operators (TMOs) compress the luminance of high dynamic range (HDR) images with little to no consideration of image color information resulting into suboptimal colors. Existing color management algorithms require either manual parameter tuning or introducing lightness and hue shifts. The current study aimed to develop a color correction model in plausible agreement with the psychophysical phenomenon of color appearance perception for optimal color reproduction of tone mapped images. The proposed model leverages CIECAM16 perceptual correlates i.e., lightness, chroma, and hue. It is adaptive and entirely automatic while preserving the lightness of the tone-mapped image and maintaining the hue from the HDR image. Moreover, it does not require any gamut mapping correction algorithm in natural color reproduction. The effectiveness of the model was validated using objective and subjective methods. The experiments revealed that the model achieved optimal color reproduction in terms of the naturalness of the colors.

Evaluation of the performance of digital x-ray systems in pelvis radiography

The aim of this study was to investigate the performance of eight digital radiography systems and to optimise the dose-image quality relationship for digital pelvis radiography. The study involved eight digital radiography systems used for general examinations at Vilnius University Hospital Santaros Klinikos. An anthropomorphic pelvic phantom (CIRS, US) was used to simulate a patient undergoing clinical pelvis radiography. Dose quantities entrance surface dose, dose area product (DAP) and exposure parameters (kVp, mA, mAs) were measured and the effects on the images were evaluated, considering physical contrast to noise ratio (CNR) and observer-based evaluations as image quality metrics. Increasing the tube voltage by 5 kVp from standard protocol led to a reduction in radiation dose (DAP) by 12%–20% with a slight impact on image quality (CNR decreases by 2%–10%). There was an inter-observer variability in image rating across different equipment (kappa value between 0 and 0.3); however, both observers agreed that increasing kVp up to 85–90 kV had no effect on perceived image quality. The results indicate that optimisation strategies should be tailored specifically for each x-ray system since significant performance differences and wide variations in radiation dose exist across various digital radiography systems used in clinical settings. The use of high kVp can be used for dose optimisation in digital pelvis radiography without compromising image diagnostic accuracy.

Cone-beam CT with a noncircular (sine-on-sphere) orbit: imaging performance of a clinical system for image-guided interventions.

We aim to compare the imaging performance of a cone-beam CT (CBCT) imaging system with noncircular scan protocols (sine-on-sphere) to a conventional circular orbit. A biplane C-arm system (ARTIS Icono; Siemens Healthineers) capable of circular and noncircular CBCT acquisition was used, with the latter orbit (sine-on-sphere, "Sine Spin") executing a sinusoidal motion with tilt amplitude over the half-scan orbit. A test phantom was used for the characterization of image uniformity, noise, noise-power spectrum (NPS), spatial resolution [modulation transfer function (MTF) in axial and oblique directions], and cone-beam artifacts. Findings were interpreted using an anthropomorphic head phantom with respect to pertinent tasks in skull base neurosurgery. The noncircular scan protocol exhibited several advantages associated with improved 3D sampling-evident in the NPS as filling of the null cone about the spatial frequency axis and reduction of cone-beam artifacts. The region of support at the longitudinal extrema was reduced from 16 to at a radial distance of 6.5cm. Circular and noncircular orbits exhibited nearly identical image uniformity and quantum noise, demonstrating cupping of and overall noise of . Although both the radially averaged axial MTF ( ) and 45deg oblique MTF ( ) were lower for the noncircular orbit compared with the circular orbit at the default full reconstruction field of view (FOV), there was no difference in spatial resolution for the medium reconstruction FOV (smaller voxel size). Differences in the perceptual image quality for the anthropomorphic phantom reinforced the objective, quantitative findings, including reduced beam-hardening and cone-beam artifacts about structures of interest in the skull base. Image quality differences between circular and noncircular CBCT orbits were quantitatively evaluated on a clinical system in the context of neurosurgery. The primary performance advantage for the noncircular orbit was the improved sampling and elimination of cone-beam artifacts.

Preclinical and Preliminary Evaluation of Perceived Image Quality of AI-Processed Low-Dose CBCT Analysis of a Single Tooth.

This study assessed AI-processed low-dose cone-beam computed tomography (CBCT) images for single-tooth diagnosis. Human-equivalent phantoms were used to evaluate CBCT image quality with a focus on the right mandibular first molar. Two CBCT machines were used for evaluation. The first CBCT machine was used for the experimental group, in which images were acquired using four protocols and enhanced with AI processing to improve quality. The other machine was used for the control group, where images were taken in one protocol without AI processing. The dose-area product (DAP) was measured for each protocol. Subjective clinical image quality was assessed twice by five dentists, with a 2-month interval in between, using 11 parameters and a six-point rating scale. Agreement and statistical significance were assessed with Fleiss' kappa coefficient and intra-class correlation coefficient. The AI-processed protocols exhibited lower DAP/field of view values than non-processed protocols, while demonstrating subjective clinical evaluation results comparable to those of non-processed protocols. The Fleiss' kappa coefficient value revealed statistical significance and substantial agreement. The intra-class correlation coefficient showed statistical significance and almost perfect agreement. These findings highlight the importance of minimizing radiation exposure while maintaining diagnostic quality as the usage of CBCT increases in single-tooth diagnosis.

16‐3: Distinguished Paper: Vergence‐Accommodation Conflict Increases Time to Focus in Augmented Reality

Vergence‐Accommodation Conflicts (VAC) occur in neareye displays when the binocular disparity of the 3D rendered content (vergence) does not match the display focal distance (accommodation). VAC has been shown to reduce perceptual image quality, cognitive performance, and oculomotor coordination. In this study, we aimed to investigate the impact of VAC on visual performance in augmented reality (AR). Specifically, we quantified the impact of AR VAC on the ‘Time to Focus’ (TTF); when the user switches focus between real world content and worldlocked AR rendered content. Our results show that TTF increases exponentially with VAC. The increase is more pronounced at closer vergence distances in displays with focal distance of 1 D or longer. Finally, we showed that VAC may have a differential effect across age groups; specifically, older users may be affected more in closer focal and longer vergence distances 186‐189.

No reference image quality assessment using gabor convolutional neural networks

With the emergence of image capturing devices and increase in usage of internet, massive volume of network data was occupied with digital images. For efficient data transmission, the image may undergo several processing units in from the point it captures to the display/storage device. It may result in loss of the perceptual image quality. Therefore, it is necessary to estimate the image quality to measure the quality of experience. It was found that the convolutional neural networks serve as potential tool for effective feature extraction in many image processing applications. Particularly, with the first layer as Gabor filters, the robustness of the network can be reinforced with learnable Gabor parameters. This paper proposes Gabor Convolutional Neural Network method for No-Reference Image Quality Assessment. Their well-defined spatial structured filters are promising in extracting quality features from the local patches and maps them to perceptual quality scores. Our proposed architecture was tested over synthetic and authentic databases such as LIVE, TID2013, CSIQ, LIVE-MD, MDID2016, LIVE Wild and KoNiQ-10k. The proposed approach was also tested on the Waterloo 3D phase-II database, which contains high-resolution images of both the eyes individually with their respective DMOS scores. The proposed approach out performs over LIVE-MD and LIVE Wild and competes with existing algorithm over other databases.

DistilIQA: Distilling Vision Transformers for no-reference perceptual CT image quality assessment

No-reference image quality assessment (IQA) is a critical step in medical image analysis, with the objective of predicting perceptual image quality without the need for a pristine reference image. The application of no-reference IQA to CT scans is valuable in providing an automated and objective approach to assessing scan quality, optimizing radiation dose, and improving overall healthcare efficiency. In this paper, we introduce DistilIQA, a novel distilled Vision Transformer network designed for no-reference CT image quality assessment. DistilIQA integrates convolutional operations and multi-head self-attention mechanisms by incorporating a powerful convolutional stem at the beginning of the traditional ViT network. Additionally, we present a two-step distillation methodology aimed at improving network performance and efficiency. In the initial step, a ”teacher ensemble network” is constructed by training five vision Transformer networks using a five-fold division schema. In the second step, a ”student network”, comprising of a single Vision Transformer, is trained using the original labeled dataset and the predictions generated by the teacher network as new labels. DistilIQA is evaluated in the task of quality score prediction from low-dose chest CT scans obtained from the LDCT and Projection data of the Cancer Imaging Archive, along with low-dose abdominal CT images from the LDCTIQAC2023 Grand Challenge. Our results demonstrate DistilIQA‘s remarkable performance in both benchmarks, surpassing the capabilities of various CNNs and Transformer architectures. Moreover, our comprehensive experimental analysis demonstrates the effectiveness of incorporating convolutional operations within the ViT architecture and highlights the advantages of our distillation methodology.

Enhanced Solar Coronal Imaging: A GAN Approach with Fused Attention and Perceptual Quality Enhancement

The activity of the solar corona has a significant impact on all aspects of human life. People typically use images obtained from astronomical telescopes to observe coronal activities, among which the Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) is particularly widely used. However, due to resolution limitations, we have begun to study the application of generative adversarial network super-resolution techniques to enhance the image data quality for a clearer observation of the fine structures and dynamic processes in the solar atmosphere, which improves the prediction accuracy of solar activities. We aligned SDO/AIA images with images from the High-Resolution Coronal Imager (Hi-C) to create a dataset. This research proposes a new super-resolution method named SAFCSRGAN, which includes a spatial attention module that incorporates channel information, allowing the network model to better capture the corona’s features. A Charbonnier loss function was introduced to enhance the perceptual quality of the super-resolution images. Compared to the original method using ESRGAN, our method achieved an 11.9% increase in Peak Signal-to-Noise Ratio (PSNR) and a 4.8% increase in Structural Similarity (SSIM). Additionally, we introduced two perceptual image quality assessment metrics, the Natural Image Quality Evaluator (NIQE) and Learned Perceptual Image Patch Similarity (LPIPS), which improved perceptual quality by 10.8% and 1.3%, respectively. Finally, our experiments demonstrated that our improved model surpasses other models in restoring the details of coronal images.

Efficient image denoising with heterogeneous kernel-based CNN

Recent advancements in deep learning have notably advanced the field of image denoising. Yet, blindly increasing the depth or width of convolutional neural networks (CNNs) cannot ameliorate the network effectively, and even leads to training difficulties and sophisticated training tricks. In this paper, a lightweight CNN with heterogeneous kernels (HKCNN) is designed for efficient noise removal. HKCNN comprises four modules: a multiscale block (MB), an attention enhancement block (AEB), an elimination block (EB), and a construct block (CB). Specifically, the MB leverages heterogeneous kernels alongside re-parameterization to capture diverse complementary structure information, bolstering discriminative ability and the denoising robustness of the denoiser. The AEB incorporates an attention mechanism that prioritizes salient features, expediting the training stage and boosting denoising efficacy. The EB and CB are designed to further suppress noise and reconstruct latent clean images. Besides, the HKCNN integrates perceptual loss for both retaining semantic details and improving image perceptual quality, so as to refine the denoising output. Comprehensive qualitative and quantitative evaluations highlight the superior performance of HKCNN over state-of-the-art denoising methods, validating its efficacy in practical image denoising scenarios.

Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time.

Deep-learning-based reconstruction (DLR) improves the quality of magnetic resonance (MR) images which allows faster acquisitions. The aim of this study was to compare the image quality of standard and accelerated T2 weighted turbo-spin-echo (TSE) images of the prostate reconstructed with and without DLR and to find associations between perceived image quality and calculated image characteristics. In a cohort of 47 prospectively enrolled consecutive patients referred for bi-parametric prostate magnetic resonance imaging (MRI), two T2-TSE acquisitions in the transverse plane were acquired on a 3T scanner-a standard T2-TSE sequence and a short sequence accelerated by a factor of two using compressed sensing (CS). The images were reconstructed with and without DLR in super-resolution mode. The image quality was rated in six domains. Signal-to-noise ratio (SNR), and image sharpness were measured. The mean acquisition time was 281±23 s for the standard and 140±12 s for the short acquisition (P<0.0001). DLR images had higher sharpness compared to non-DLR (P<0.001). Short and short-DLR had lower SNR than the standard and standard-DLR (P<0.001). The perceived image quality of short-DLR was rated better in all categories compared to the standard sequence (P<0.001 to P=0.004). All domains of subjective evaluation were correlated with measured image sharpness (P<0.001). T2-TSE acquisition of the prostate accelerated using CS combined with DLR reconstruction provides images with increased sharpness that have a superior quality as perceived by human readers compared to standard T2-TSE. The perceived image quality is correlated with measured image contrast.

DCID: A divide and conquer approach to solving the trade-off problem between artifacts caused by enhancement procedure in image downscaling

Conventional research on image downscaling is conducted to improve the visual quality of the resultant downscaled image. However, there is an intractable problem, a trade-off relationship between artifacts such as aliasing and ringing, caused by enhancement procedure in image downscaling. To solve this problem, we propose a novel method that applies a divide-and-conquer approach for image downscaling (DCID). Specifically, the proposed DCID includes Weight-Net for dividing regions into enhancement first and artifact-least first regions and two generators that are optimized for divided regions to conquer the trade-off problem in the image downscaling task. The proposed method can generate a downscaled image without creating artifacts while preserving the perceptual quality of the input image. In objective and subjective evaluations, our experimental results show that the quality of the downscaled images generated by the proposed DCID is significantly better than benchmark methods.

Omnidirectional Virtual Visual Acuity: A User-Centric Visual Clarity Metric for Virtual Reality Head-Mounted Displays and Environments.

Users' perceived image quality of virtual reality head-mounted displays (VR HMDs) is determined by multiple factors, including the HMD's structure, optical system, display and render resolution, and users' visual acuity (VA). Existing metrics such as pixels per degree (PPD) have limitations that prevent accurate comparison of different VR HMDs. One of the main limitations is that not all VR HMD manufacturers released the official PPD or details of their HMDs' optical systems. Without these details, developers and users cannot know the precise PPD or calculate it for a given HMD. The other issue is that the visual clarity varies with the VR environment. Our work has identified a gap in having a feasible metric that can measure the visual clarity of VR HMDs. To address this gap, we present an end-to-end and user-centric visual clarity metric, omnidirectional virtual visual acuity (OVVA), for VR HMDs. OVVA extends the physical visual acuity chart into a virtual format to measure the virtual visual acuity of an HMD's central focal area and its degradation in its noncentral area. OVVA provides a new perspective to measure visual clarity and can serve as an intuitive and accurate reference for VR applications sensitive to visual accuracy. Our results show that OVVA is a simple yet effective metric for comparing VR HMDs and environments.