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A no-reference objective metric for image quality assessment by integrating the topographic independent components analysis into feature extraction is presented. By taking the topographic relationship among the initially independent features into consideration, it extracts the features of more sparsity or independency which is essentially related to inherent quality. Evaluation results demonstrate that the proposed metric is able to predict the image quality accurately across various distortion types.

Remotely sensed land surface temperature (LST) distribution has played a valuable role in land surface processes studies from local to global scales. However, it is still difficult to acquire concurrently high spatiotemporal resolution LST data due to the trade-off between spatial and temporal resolutions in thermal remote sensing. To address this problem, various methods have been proposed to enhance the resolutions of LST data, and substantial progress in this field has been achieved in recent years. Therefore, this study reviewed the current status of resolution enhancement methods for LST data. First, three groups of enhancement methods—spatial resolution enhancement, temporal resolution enhancement, and simultaneous spatiotemporal resolution enhancement—were comprehensively investigated and analyzed. Then, the quality assessment strategies for LST resolution enhancement methods and their advantages and disadvantages were specifically discussed. Finally, key directions for future studies in this field were suggested, i.e., synergy between process-driven and data-driven methods, cross-comparison among different methods, and improvement in localization strategy.

Given the importance of turbidity as a key indicator of water quality, this study investigates the use of a convolutional neural network (CNN) to classify water samples into five turbidity-based categories. These classes were defined using ranges inspired by Mexican environmental regulations and generated from 33 laboratory-prepared mixtures with varying concentrations of suspended clay particles. Red, green, and blue (RGB) images of each sample were captured under controlled optical conditions, and turbidity was measured using a calibrated turbidimeter. A transfer learning (TL) approach was applied using EfficientNet-B0, a deep yet computationally efficient CNN architecture. The model achieved an average accuracy of 99% across ten independent training runs, with minimal misclassifications. The use of a lightweight deep learning model, combined with a standardized image acquisition protocol, represents a novel and scalable alternative for rapid, low-cost water quality assessment in future environmental monitoring systems.

PurposeTo obtain the optimal simultaneous-multislice (SMS)—accelerated diffusion-weighted imaging (DWI) of the liver at 3.0 T MRI by systematically estimating the repeatability of apparent diffusion coefficient (ADC), signal-to-noise ratio (SNR) and image quality of different breathing schemes in comparison to standard DWI (STD) and other SMS sequences.MethodsIn this institutional review board-approved prospective study, hepatic DWIs (b = 50, 300, 600 s/mm2) were performed in 23 volunteers on 3.0 T MRI using SMS and STD with breath-hold (BH-SMS, BH-STD), free-breathing (FB-SMS, FB-STD) and respiratory-triggered (RT-SMS, RT-STD). Reduction of scan time with SMS-acceleration was calculated. ADC and SNR were measured in nine anatomic locations and image quality was assessed on all SMS and STD sequences. An optimal SMS-DWI was decided by systematically comparing the ADC repeatability, SNR and image quality among above DWIs.ResultsSMS-DWI reduced scan time significantly by comparison with corresponding STD-DWI (27 vs. 42 s for BH, 54 vs. 78 s for FB and 42 vs. 97 s for RT). In all DWIs, BH-SMS had the greatest intraobserver agreement (intraclass correlation coefficient (ICC): 0.920–0.944) and good interobserver agreement (ICC: 0.831–0.886) for ADC measurements, and had the best ADC repeatability (mean ADC absolute differences: 0.046–0.058 × 10−3mm2/s, limits of agreement (LOA): 0.010–0.013 × 10−3mm2/s) in nine locations. BH-SMS had the highest SNR in three representative sections except for RT-STD. There were no significant differences in image quality between BH-SMS and other DWI sequences (median BH-SMS: 4.75, other DWI: 4.5–5.0; P > 0.0.5).ConclusionBH-SMS provides considerable scan time reduction with good image quality, sufficient SNR and highest ADC repeatability on 3.0 T MRI, which is thus recommended as the optimal hepatic DWI sequence for those subjects with adequate breath-holding capability.

Digital radiography is widely seen to be forgiving of poor exposure technique and to provide consistent high quality diagnostic images. Optimal quality images are however not universal; sub-optimal images are encountered. Evaluators on hip dysplasia schemes encounter images from multiple practices produced on equipment from multiple manufacturers. For images submitted to the Danish Kennel Club for hip dysplasia screening, a range of quality is seen and the evaluators are of the impression that variations in image quality area associated with particular equipment. This study was undertaken to test the hypothesis that there is an association between image quality in digital radiography and the manufacturer of the detector equipment, and to demonstrate the applicability of visual grading analysis (VGA) for image quality evaluation in veterinary practice. Data from 16,360 digital images submitted to the Danish Kennel Club were used to generate the hypothesis that there is an association between detector manufacturer and image quality and to create groups for VGA. Image quality in a subset of 90 images randomly chosen from 6 manufacturers to represent high and low quality images, was characterized using VGA and the results used to test for an association between image quality and system manufacturer. The range of possible scores in the VGA was -2 to +2 (higher scores are better). The range of the VGA scores for the images in the low image quality group ( = 45) was -1.73 to +0.67, (median -1.2). Images in the high image quality group ( = 44) ranged from -1.52 to +0.53, (median -0.53). This difference was statistically significant ( < 0.001). The study shows an association between VGA scores of image quality and detector manufacturer. Possible causes may be that imaging hardware and/or software are not equal in terms of quality, that the level of support sought and given differs between systems, or a combination of the two. Clinicians purchasing equipment should be mindful that image quality can differ across systems. VGA is practical for veterinarians to compare image quality between systems or within a system over time.

Pharmacovigilance is the scientific discipline that copes with the continuous assessment of the safety profile of marketed drugs. This assessment relies on diverse data sources, which are routinely analysed to identify the so-called “signals”, i.e. potential associations between drugs and adverse effects, that are unknown or incompletely documented. Various computational methods have been proposed to support domain experts in signal detection. However, recent comparative studies illustrated that current methods exhibit high false-positive rates, significantly variable performance across different datasets used for analysis and events of interest, but also complementarity in their outcomes. In this regard, in order to reinforce accurate and timely signal detection, we elaborated through an agent-based approach towards systematic, joint exploitation of multiple heterogeneous signal detection methods, data sources and other drug-related resources under a common, integrated framework. The approach relies on a multiagent system operating based on a collaborative agent interaction protocol, aiming to implement a comprehensive workflow that comprises of method selection and execution, as well as outcomes’ aggregation, filtering, ranking and annotation. This paper presents the design of the proposed multiagent system, discusses implementation issues and demonstrates the applicability of the proposed solution in an example signal detection scenario. This work constitutes a step towards large-scale, integrated and knowledge-intensive computational signal detection.