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IEEE Transactions on Image Processing :... 2024Effectively evaluating the perceptual quality of dehazed images remains an under-explored research issue. In this paper, we propose a no-reference complex-valued...
Effectively evaluating the perceptual quality of dehazed images remains an under-explored research issue. In this paper, we propose a no-reference complex-valued convolutional neural network (CV-CNN) model to conduct automatic dehazed image quality evaluation. Specifically, a novel CV-CNN is employed that exploits the advantages of complex-valued representations, achieving better generalization capability on perceptual feature learning than real-valued ones. To learn more discriminative features to analyze the perceptual quality of dehazed images, we design a dual-stream CV-CNN architecture. The dual-stream model comprises a distortion-sensitive stream that operates on the dehazed RGB image, and a haze-aware stream on a novel dark channel difference image. The distortion-sensitive stream accounts for perceptual distortion artifacts, while the haze-aware stream addresses the possible presence of residual haze. Experimental results on three publicly available dehazed image quality assessment (DQA) databases demonstrate the effectiveness and generalization of our proposed CV-CNN DQA model as compared to state-of-the-art no-reference image quality assessment algorithms.
PubMed: 38150345
DOI: 10.1109/TIP.2023.3343029 -
Frontiers in Psychology 2023Visual perception is a complex process that involves the analysis of different spatial and temporal features of the visual environment. One critical aspect of this...
INTRODUCTION
Visual perception is a complex process that involves the analysis of different spatial and temporal features of the visual environment. One critical aspect of this process is adaptation, which allows the visual system to adjust its sensitivity to specific features based on the context of the environment. Numerous theories highlight the significance of the visual scene and its spectral properties in perceptual and adaptation mechanisms. For example, size perception is known to be influenced by the spatial frequency content of the visual scene. Nonetheless, several inquiries still exist, including how specific spectral properties of the scene play a role in size perception and adaptation mechanisms.
METHODS
In this study, we explore aftereffects on size perception following adaptation to a natural scene with a biased spectral amplitude distribution. Twenty participants had to manually estimate the horizontal size of a projected rectangle after adaptation to three visually biased conditions: vertical-biased, non-biased, and horizontal-biased. Size adaptation aftereffects were quantified by comparing the perceptual responses from the non-biased condition with the vertical- and horizontal-biased conditions.
RESULTS
We found size perception shifts which were contingent upon the specific orientation and spatial frequency distribution inherent in the amplitude spectra of the adaptation stimuli. Particularly, adaptation to vertical-biased produced a horizontal enlargement, while adaptation to horizontal-biased generated a decrease in the horizontal size perception of the rectangle. On average, size perception was modulated by 5-6%.
DISCUSSION
These findings provide supporting evidence for the hypothesis that the neural mechanisms responsible for processing spatial frequency channels are involved in the encoding and perception of size information. The implications for neural mechanisms underlying spatial frequency and size information encoding are discussed.
PubMed: 38125858
DOI: 10.3389/fpsyg.2023.1247687 -
West African Journal of Medicine Dec 2023Schizophrenia is a severe mental health disorder characterized by abnormality in patient perception, belief and cognition resulting in gross abnormal behaviour and...
BACKGROUND
Schizophrenia is a severe mental health disorder characterized by abnormality in patient perception, belief and cognition resulting in gross abnormal behaviour and deterioration in interpersonal relationship and occupational functioning with onset usually in adolescence and youth period. While it is common to observe distortion in the belief system and perceptual experiences and other oddities of behaviour, including amotivational syndrome, ambivalence, social withdrawal, catatonia among youth suffering from this severe mental disorder, presenting with neurological symptom of complete inability to walk despite the desire to do so is very uncommon. We aimed to present a case report of a Nigerian youth who presented with inability to walk without any neurological deficit and had normal brain MRI scan. This is to highlight the need to have high index of suspicion among practitioner especially in young person with sudden onset of "paraplegia".
CASE PRESENTATION
Mr X is a 30 years old single Lecturer who was brought into the hospital by relatives with two weeks history of sudden onset of inability to walk around and became bed bound. He graduated with first class in Mass Communication and commenced National Service as Lecturer when he started hearing voices discussing him and also believes that people want to kill. He became reclusive to self for about two years. No history of trauma to the head or hypertensive heart disease. He was earlier seen by a doctor who commenced him on carbamazepine and olanzapine but drugs adherent was poor and later completely abandon for unorthodox treatment. MRI Scan of the brain, FBC + diff, U & E and neurological examination were normal. He was commence on Risperdal and six weeks later into the treatment, he started ambulating about.
CONCLUSION
While it may be rare, severe psychotic illness such as schizophrenia can result in complete inability to walk, mimicking neurological disorder. High index of suspicion with perseverance of treatment can resolve the patient illness and restore his social life.
Topics: Adult; Humans; Male; Paraplegia; Schizophrenia
PubMed: 38071483
DOI: No ID Found -
Journal of Psychopharmacology (Oxford,... Mar 2024The 'beer goggles' phenomenon describes sexual attraction to individuals when alcohol intoxicated whom we would not desire when sober. One possible explanation of the...
BACKGROUND
The 'beer goggles' phenomenon describes sexual attraction to individuals when alcohol intoxicated whom we would not desire when sober. One possible explanation of the effect is that alcohol impairs the detection of facial asymmetry, thus lowering the drinker's threshold for physical attraction.
AIMS
We therefore tested the hypotheses that higher breath alcohol drinkers would award more generous ratings of attractiveness to asymmetrical faces, and be poorer at discriminating bilateral facial asymmetry than less intoxicated counterparts.
METHODS
Ninety-nine male and female bar patrons rated 18 individual faces for attractiveness and symmetry. Each type of rating was given twice, once per face with an enhanced asymmetry and once again for each face in its natural form. Participants then judged which of two same-face versions (one normal, the other perfectly symmetrised) was more attractive and, in the final task, more symmetrical.
RESULTS
Alcohol had no influence on attractiveness judgements but higher blood alcohol concentrations were associated with higher symmetry ratings. Furthermore, as predicted, heavily intoxicated individuals were less able to distinguish natural from perfectly symmetrised face versions than more sober drinkers.
CONCLUSIONS
Findings therefore suggest alcohol impairs face asymmetry detection, but it seems that this perceptual distortion does not contribute to the 'beer goggles' phenomenon.
Topics: Humans; Male; Female; Facial Asymmetry; Face; Beer; Eye Protective Devices; Beauty; Ethanol
PubMed: 38069489
DOI: 10.1177/02698811231215592 -
IEEE Transactions on Visualization and... Dec 2023The goal of objective point cloud quality assessment (PCQA) research is to develop quantitative metrics that measure point cloud quality in a perceptually consistent...
The goal of objective point cloud quality assessment (PCQA) research is to develop quantitative metrics that measure point cloud quality in a perceptually consistent manner. Merging the research of cognitive science and intuition of the human visual system (HVS), in this paper, we evaluate the point cloud quality by measuring the complexity of transforming the distorted point cloud back to its reference, which in practice can be approximated by the code length of one point cloud when the other is given. For this purpose, we first make space segmentation for the reference and distorted point clouds based on a 3D Voronoi diagram to obtain a series of local patch pairs. Next, inspired by the predictive coding theory, we utilize a space-aware vector autoregressive (SA-VAR) model to encode the geometry and color channels of each reference patch with and without the distorted patch, respectively. Assuming that the residual errors follow the multi-variate Gaussian distributions, the self-complexity of the reference and transformational complexity between the reference and distorted samples are computed using covariance matrices. Additionally, the prediction terms generated by SA-VAR are introduced as one auxiliary feature to promote the final quality prediction. The effectiveness of the proposed transformational complexity based distortion metric (TCDM) is evaluated through extensive experiments conducted on five public point cloud quality assessment databases. The results demonstrate that TCDM achieves state-of-the-art (SOTA) performance, and further analysis confirms its robustness in various scenarios. The code will be publicly available at https://github.com/zyj1318053/TCDM.
PubMed: 38039169
DOI: 10.1109/TVCG.2023.3338359 -
Nature Communications Nov 2023Introspective agents can recognize the extent to which their internal perceptual experiences deviate from the actual states of the external world. This ability, also...
Introspective agents can recognize the extent to which their internal perceptual experiences deviate from the actual states of the external world. This ability, also known as insight, is critically required for reality testing and is impaired in psychosis, yet little is known about its cognitive underpinnings. We develop a Bayesian modeling framework and a psychophysics paradigm to quantitatively characterize this type of insight while people experience a motion after-effect illusion. People can incorporate knowledge about the illusion into their decisions when judging the actual direction of a motion stimulus, compensating for the illusion (and often overcompensating). Furthermore, confidence, reaction-time, and pupil-dilation data all show signatures consistent with inferential adjustments in the Bayesian insight model. Our results suggest that people can question the veracity of what they see by making insightful inferences that incorporate introspective knowledge about internal distortions.
Topics: Humans; Perceptual Distortion; Illusions; Bayes Theorem; Psychophysics; Psychotic Disorders; Motion Perception
PubMed: 38030601
DOI: 10.1038/s41467-023-42813-2 -
Sensors (Basel, Switzerland) Nov 2023Images captured during marine engineering operations suffer from color distortion and low contrast. Underwater image enhancement helps to alleviate these problems. Many...
Images captured during marine engineering operations suffer from color distortion and low contrast. Underwater image enhancement helps to alleviate these problems. Many deep learning models can infer multi-source data, where images with different perspectives exist from multiple sources. To this end, we propose a multichannel deep convolutional neural network (MDCNN) linked to a VGG that can target multi-source (multi-domain) underwater image enhancement. The designed MDCNN feeds data from different domains into separate channels and implements parameters by linking VGGs, which improves the domain adaptation of the model. In addition, to optimize performance, multi-domain image perception loss functions, multilabel soft edge loss for specific image enhancement tasks, pixel-level loss, and external monitoring loss for edge sharpness preprocessing are proposed. These loss functions are set to effectively enhance the structural and textural similarity of underwater images. A series of qualitative and quantitative experiments demonstrate that our model is superior to the state-of-the-art Shallow UWnet in terms of UIQM, and the performance evaluation conducted on different datasets increased by 0.11 on average.
PubMed: 37960682
DOI: 10.3390/s23218983 -
Cureus Sep 2023Hallucinogen-persisting perception disorder (HPPD), also known as acute hallucinogen-induced psychosis or informally known as "flashbacks," is an unusual condition...
Hallucinogen-persisting perception disorder (HPPD), also known as acute hallucinogen-induced psychosis or informally known as "flashbacks," is an unusual condition experienced by patients due to the use of different hallucinogenic substances. Hallucinogen-persisting perception disorder causes many symptoms, predominantly persistent visual perception distortion instead of intermittent distortion. Although different hallucinogens could cause HPPD, lysergic acid diethylamide (LSD) and LSD-like properties seem to be the most common hallucinogens causing the symptoms. In our case report, the patient is a 28-year-old Caucasian male with a long psychiatric and social history of polysubstance use using LSD and cannabis. He started experiencing many of the classic symptoms of HPPD seven months after stopping LSD. The diagnosis is suspected by ruling out all other possible underlying causes with the help of several laboratory and imaging tests. Despite having an extensive psychiatric history of illnesses, the patient's symptoms failed to improve with antipsychotics, confirming that the symptoms were not only due to mental illness. Although supposedly the first-line treatment for HPPD is the use of alpha-2 adrenergic drugs such as clonidine and benzodiazepines, we started to witness improvement in patient's symptoms with the use of lamotrigine, which is the gold standard in treating perceptual disturbance in time and space.
PubMed: 37908914
DOI: 10.7759/cureus.46262 -
IEEE Transactions on Image Processing :... 2023Dynamic point cloud is a volumetric visual data representing realistic 3D scenes for virtual reality and augmented reality applications. However, its large data volume...
Dynamic point cloud is a volumetric visual data representing realistic 3D scenes for virtual reality and augmented reality applications. However, its large data volume has been the bottleneck of data processing, transmission, and storage, which requires effective compression. In this paper, we propose a Perceptually Weighted Rate-Distortion Optimization (PWRDO) scheme for Video-based Point Cloud Compression (V-PCC), which aims to minimize the perceptual distortion of reconstructed point cloud at the given bit rate. Firstly, we propose a general framework of perceptually optimized V-PCC to exploit visual redundancies in point clouds. Secondly, a multi-scale Projection based Point Cloud quality Metric (PPCM) is proposed to measure the perceptual quality of 3D point cloud. The PPCM model comprises 3D-to-2D patch projection, multi-scale structural distortion measurement, and fusion model. Approximations and simplifications of the proposed PPCM are also presented for both V-PCC integration and low complexity. Thirdly, based on the simplified PPCM model, we propose a PWRDO scheme with Lagrange multiplier adaptation, which is incorporated into the V-PCC to enhance the coding efficiency. Experimental results show that the proposed PPCM models can be used as standalone quality metrics, and they are able to achieve higher consistency with the human subjective scores than the state-of-the-art objective visual quality metrics. Also, compared with the latest V-PCC reference model, the proposed PWRDO-based V-PCC scheme achieves an average bit rate reduction of 13.52%, 8.16%, 10.56% and 9.54%, respectively, in terms of four objective visual quality metrics for point clouds. It is significantly superior to the state-of-the-art coding algorithms. The computational complexity of the proposed PWRDO increases by 1.71% and 0.05% on average to the V-PCC encoder and decoder, respectively, which is negligible. The source codes of the PPCM and PWRDO schemes are available at https://github.com/VVCodec/PPCM-PWRDO.
PubMed: 37903048
DOI: 10.1109/TIP.2023.3327003 -
Journal of Magnetic Resonance Imaging :... Jun 2024Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis.
BACKGROUND
Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis.
PURPOSE
To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm.
STUDY TYPE
Retrospective.
SUBJECTS
615 consecutive patients (median age 67 [interquartile range [IQR]: 61-71] years) with elevated serum PSA (median PSA 6.6 [IQR: 4.6-9.8] ng/mL) prior to prostate biopsy.
FIELD STRENGTH/SEQUENCE
3.0T/T2-weighted turbo-spin-echo MRI, high b-value echo-planar diffusion-weighted imaging, and gradient recalled echo dynamic contrast-enhanced.
ASSESSMENTS
Scans were prospectively evaluated during clinical readout using PI-RADSv2.1 by one genitourinary radiologist with 17 years of experience. For each patient, T2-weighted images (T2WIs) were classified as high-quality or low-quality based on evaluation of both general distortions (eg, motion, distortion, noise, and aliasing) and perceptual distortions (eg, obscured delineation of prostatic capsule, prostatic zones, and excess rectal gas) by a previously developed in-house AI algorithm. Patients with PI-RADS category 1 underwent 12-core ultrasound-guided systematic biopsy while those with PI-RADS category 2-5 underwent combined systematic and targeted biopsies. Patient-level cancer detection rates (CDRs) were calculated for clinically significant prostate cancer (csPCa, International Society of Urological Pathology Grade Group ≥2) by each biopsy method and compared between high- and low-quality images in each PI-RADS category.
STATISTICAL TESTS
Fisher's exact test. Bootstrap 95% confidence intervals (CI). A P value <0.05 was considered statistically significant.
RESULTS
385 (63%) T2WIs were classified as high-quality and 230 (37%) as low-quality by AI. Targeted biopsy with high-quality T2WIs resulted in significantly higher clinically significant CDR than low-quality images for PI-RADS category 4 lesions (52% [95% CI: 43-61] vs. 32% [95% CI: 22-42]). For combined biopsy, there was no significant difference in patient-level CDRs for PI-RADS 4 between high- and low-quality T2WIs (56% [95% CI: 47-64] vs. 44% [95% CI: 34-55]; P = 0.09).
DATA CONCLUSION
Higher quality T2WIs were associated with better targeted biopsy clinically significant cancer detection performance for PI-RADS 4 lesions. Combined biopsy might be needed when T2WI is lower quality.
LEVEL OF EVIDENCE
2 TECHNICAL EFFICACY: Stage 1.
Topics: Humans; Male; Prostatic Neoplasms; Deep Learning; Middle Aged; Aged; Retrospective Studies; Magnetic Resonance Imaging; Prostate; Algorithms; Image Interpretation, Computer-Assisted; Prostate-Specific Antigen; Image Processing, Computer-Assisted
PubMed: 37811666
DOI: 10.1002/jmri.29031