Image Luminance Research Articles

Intensity grading of kidney biopsy direct immunofluorescence IgG image via semantically enhanced feature network

The intensity of direct immunofluorescence IgG imaging for kidney biopsy is an important reference index for the diagnosis of common chronic kidney diseases (CKD) such as membranous nephropathy (MN). Although this index is widely used in diagnosing membranous nephropathy, the intensity levels are currently judged only by the subjective perception of fluorescence luminance in pathological images through physicians’ naked eyes. Therefore, consistent intensity level classification cannot be achieved. To address this issue, this paper proposes a Preprocessing Semantically Enhanced Feature image classification method. The segmentation network is used to segment the original kidney biopsy direct immunofluorescence IgG images to isolate the diseased glomeruli. These segmented glomeruli images are then compared with the original glomeruli images to standardize the kidney biopsy direct immunofluorescence images. Simultaneously, considering the characteristics of small inter-class differences and large intra-class differences in fine-grained images, a semantically enhanced feature image classification model is employed to automatically classify the intensity of the standardized images. The method was evaluated on a constructed real clinical dataset, and the experimental results demonstrated that the proposed method achieved high classification accuracy on this dataset. The source code is made available on https://github.com/SnowRain510/PSEF-NET

Fixational eye movements and their associated evoked potentials during natural vision are altered in schizophrenia

BackgroundVisual exploration is abnormal in schizophrenia; however, few studies have investigated the physiological responses during selecting objectives in more ecological scenarios. This study aimed to demonstrate that people with schizophrenia have difficulties observing the prominent elements of an image due to a deficit mechanism of sensory modulation (active sensing) during natural vision. MethodsAn electroencephalogram recording with eye tracking data was collected on 18 healthy individuals and 18 people affected by schizophrenia while looking at natural images. These had a prominent color element and blinking produced by changes in image luminance. ResultsWe found fewer fixations when all images were scanned, late focus on prominent image areas, decreased amplitude in the eye-fixation-related potential, and decreased intertrial coherence in the SCZ group. ConclusionsThe decrease in the visual attention response evoked by the prominence of visual stimuli in patients affected by schizophrenia is generated by a reduction in endogenous attention mechanisms to initiate and maintain visual exploration. Further work is required to explain the relationship of this decrease with clinical indicators.

Characterisation of the Glare Properties of Photovoltaic Modules and PV Systems Using a Photographic Camera

With the increasing prevalence of photovoltaic (PV) installations on north-facing roofs in urban areas, the issue of glare on PV modules is becoming more significant. This paper presents a method for measuring and characterising the intensity and type of glare using a commercially available photo camera. The method utilises the fact that brightness information is inherently contained in a photograph. Due to the strong non-linearity of brightness information in an image, especially when using High Dynamic Range (HDR) functionality, an image needs to be divided into multiple sub-images with clearly defined settings, and the linear range extracted from each of them. The information obtained in this way is then assembled into a new image. The resulting luminance image describes the intensity and shape of glare, forming the basis for calculating the occurrence of glare (times and days of the year) in a simplified manner.

Best Practices for Measuring the Modulation Transfer Function of Video Endoscopes.

Endoscopes are crucial for assisting in surgery and disease diagnosis, including the early detection of cancer. The effective use of endoscopes relies on their optical performance, which can be characterized with a series of metrics such as resolution, vital for revealing anatomical details. The modulation transfer function (MTF) is a key metric for evaluating endoscope resolution. However, the 2020 version of the ISO 8600-5 standard, while introducing an endoscope MTF measurement method, lacks empirical validation and excludes opto-electronic video endoscopes, the largest family of endoscopes. Measuring the MTF of video endoscopes requires tailored standards that address their unique characteristics. This paper aims to expand the scope of ISO 8600-5:2020 to include video endoscopes, by optimizing the MTF test method and addressing parameters affecting measurement accuracy. We studied the effects of intensity and uniformity of image luminance, chart modulation compensation, linearity of image digital values, auto gain control, image enhancement, image compression and the region of interest dimensions on images of slanted-edge test charts, and thus the MTF based on these images. By analyzing these effects, we provided recommendations for setting and controlling these factors to obtain accurate MTF curves. Our goal is to enhance the standard's relevance and effectiveness for measuring the MTF of a broader range of endoscopic devices, with potential applications in the MTF measurement of other digital imaging devices.

Reduction of short-time image sticking in organic light-emitting diode display through transient analysis of low-temperature polycrystalline silicon thin-film transistor

Accurate compensation operation of low-temperature polycrystalline-silicon (LTPS) thin-film transistor (TFT) in pixel circuits is crucial to achieve steady and uniform luminance in organic light-emitting diode (OLED) display panels. However, the device characteristics fluctuate over time due to various traps in the LTPS thin film transistor and at the interface with the gate insulator, resulting in abnormal phenomena such as short-time image sticking and luminance fluctuation, which degrade display quality during image change. Considering these phenomena, transient analysis was conducted through device simulation to optimize the pixel compensation circuit. In particular, we analyzed the behavior of traps within LTPS TFT in correlation with compensation circuit operation, and based on this, proposed a methodology for designing a reset voltage scheme for the driver TFT to reduce the image sticking phenomenon.

Omnidirectional mid-air image system using micro-mirror array plates

We proposed and implemented an omnidirectional mid-air image optical system that suppresses stray light and transmitted light. When micro-mirror array plates (MMAP) are integrated with view control films and rotated these optical elements at high speed, stray and transmitted light are effectively suppressed. This enables the visibility of omnidirectional mid-air image. We evaluated the effects of the view control film and high-speed rotation on the luminance and resolution of mid-air images, respectively. Our system facilitates the simultaneous viewing of mid-air images by multiple users, expanding the accessibility of mid-air image content to a large audience.

Multi-Modular Network-Based Retinex Fusion Approach for Low-Light Image Enhancement

Current low-light image enhancement techniques prioritize increasing image luminance but fail to address issues including loss of intricate distortion of colors and image details. In order to address these issues that has been overlooked by all parties, this paper suggests a multi-module optimization network for enhancing low-light images by integrating deep learning with Retinex theory. First, we create a decomposition network to separate the lighting components and reflections from the low-light image. We incorporated an enhanced global spatial attention (GSA) module into the decomposition network to boost its flexibility and adaptability. This module enhances the extraction of comprehensive information from the image and safeguards against information loss. To increase the illumination component’s luminosity, we subsequently constructed an enhancement network. The Multiscale Guidance Block (MSGB) has been integrated into the improvement network, together with multilayer extended convolution to expand the sensing field and enhance the network’s capability for feature extraction. Our proposed method out-performs existing ways in both objective measures and personal evaluations, emphasizing the virtues of the procedure outlined in this paper.

Multi-modal medical image fusion using improved dual-channel PCNN.

This paper proposes a medical image fusion method in the non-subsampled shearlet transform (NSST) domain to combine a gray-scale image with the respective pseudo-color image obtained through different imaging modalities. The proposed method applies a novel improved dual-channel pulse-coupled neural network (IDPCNN) model to fuse the high-pass sub-images, whereas the Prewitt operator is combined with maximum regional energy (MRE) to construct the fused low-pass sub-image. First, the gray-scale image and luminance of the pseudo-color image are decomposed using NSST to find the respective sub-images. Second, the low-pass sub-images are fused by the Prewitt operator and MRE-based rule. Third, the proposed IDPCNN is utilized to get the fused high-pass sub-images from the respective high-pass sub-images. Fourth, the luminance of the fused image is obtained by applying inverse NSST on the fused sub-images, which is combined with the chrominance components of the pseudo-color image to construct the fused image. A total of 28 diverse medical image pairs, 11 existing methods, and nine objective metrics are used in the experiment. Qualitative and quantitative fusion results show that the proposed method is competitive with and even outpaces some of the existing medical fusion approaches. It is also shown that the proposed method efficiently combines two gray-scale images.

A Unifying Model for Discordant and Concordant Results in Human Neuroimaging Studies of Facial Viewpoint Selectivity.

Recognizing faces regardless of their viewpoint is critical for social interactions. Traditional theories hold that view-selective early visual representations gradually become tolerant to viewpoint changes along the ventral visual hierarchy. Newer theories, based on single-neuron monkey electrophysiological recordings, suggest a three-stage architecture including an intermediate face-selective patch abruptly achieving invariance to mirror-symmetric face views. Human studies combining neuroimaging and multivariate pattern analysis (MVPA) have provided convergent evidence of view selectivity in early visual areas. However, contradictory conclusions have been reached concerning the existence in humans of a mirror-symmetric representation like that observed in macaques. We believe these contradictions arise from low-level stimulus confounds and data analysis choices. To probe for low-level confounds, we analyzed images from two face databases. Analyses of image luminance and contrast revealed biases across face views described by even polynomials-i.e., mirror-symmetric. To explain major trends across neuroimaging studies, we constructed a network model incorporating three constraints: cortical magnification, convergent feedforward projections, and interhemispheric connections. Given the identified low-level biases, we show that a gradual increase of interhemispheric connections across network-layers is sufficient to replicate view-tuning in early processing stages and mirror-symmetry in later stages. Data analysis decisions-pattern dissimilarity measure and data recentering-accounted for the inconsistent observation of mirror-symmetry across prior studies. Pattern analyses of human fMRI data (of either sex) revealed biases compatible with our model. The model provides a unifying explanation of MVPA studies of viewpoint selectivity and suggests observations of mirror-symmetry originate from ineffectively normalized signal imbalances across different face views.

Visualization of water transfer channel in sludge dewatering conditioned with skeleton builders by X-ray micro-computed tomography

Skeleton builders were normally deemed to improve the high porosity and newly-generated permeability of sludge cakes by building water transfer channel during high pressure filtration, thus enhancing sludge dewaterability. However, currently a direct visualization proof of water transfer channel was still lacking. This study provided the direct proof for visualizing water transfer channel in dewatered sludge cakes conditioned with a typical skeleton builder (i.e., phosphogypsum (PG)) by X-ray micro-computed tomography (micro-CT) for the first time. After the addition of PG, the pixel value and image luminance increased significantly, indicating the presence of high density substances from both two-dimensional (2D) cross section and three-dimensional (3D) reconstruction CT images. Moreover, the CT numbers showed strong and negative correlations with specific resistance to filtration (SRF) (R = − 0.99, p < 0.05), capillary suction time (CST) (regression coefficient (R) = − 0.87, probability (p) < 0.05), and water content of the dewatered sludge cake (R = − 0.99, p < 0.05), respectively. These results indicated that the X-ray micro-CT could be a potential technique for analyzing the water distribution in sludge samples conditioned with skeleton builders.

A Low-Latency Noise-Aware Tone Mapping Operator for Hardware Implementation with a Locally Weighted Guided Filter

A tone mapping operator (TMO) is a module in the image signal processing pipeline that is used to convert high dynamic range images to low dynamic range images for display. Currently, state-of-the-art TMOs typically take complex algorithms and are implemented on graphics processing units, making it difficult to run with low latency on edge devices, and TMOs implemented in hardware circuits often lack additional noise suppression because of latency and hardware resource constraints. To address these issues, we proposed a low-latency noise-aware TMO for hardware implementation. Firstly, a locally weighted guided filter is proposed to decompose the luminance image into a base layer and a detail layer, with the weight function symmetric concerning the central pixel value of a window. Secondly, the mean and standard deviation of the basic layer and the detail layer are used to estimate the noise visibility according to the human visual characteristics. Finally, the gain for the detail layer is calculated to achieve adaptive noise suppression. In this process, luminance is first processed by the log2 function before being filtered and then symmetrically converted back to the linear domain by the exp2 function after compression. Meanwhile, the algorithms within the proposed TMO were optimized for hardware implementation to minimize latency and cache, achieving a low latency of 60.32 μs under video specification of 1080 P at 60 frames per second and objective metric smoothness in dark flat regions could be improved by more than 10% compared to similar methods.

DRC: Chromatic aberration intensity priors for underwater image enhancement

Underwater imaging technology is a crucial tool for monitoring marine flora and fauna. However, selective light absorption and scattering properties of water make underwater imagery frequently appear blurred and exhibit color biases, hindering the extraction of vital aquacultural insights. To address this challenge, we propose a method, namely DRC, which is a holistic approach to enhancing underwater image clarity and color fidelity. This method comprises three integral components: D-procedure, R-procedure, and C-procedure. The D-procedure intricately accounts for the trichromatic underwater attenuation dynamics, formulating a chromatic aberration intensity prior. This prior counters the disparities in degradation levels seen in conventional single-channel prior depth estimations, achieving a dynamic depth representation approaching binocular image precision. The R-procedure, utilizing an adaptive dark-pixel prior, pinpoints corresponding points across varied depth zones to counteract backscattering, thereby mitigating the image’s hazy appearance. The C-procedure bolsters image luminance and color fidelity through opponent channel rectification and amalgamates pronounced image contrasts and intricate details via Gaussian pyramid fusion. The method was tested on several publicly available datasets and compared with nine popular underwater image enhancement techniques. Both subjective and objective assessments underscore the superiority of our DRC method over existing underwater image enhancement techniques.

Time-of-day perception in paintings.

The spectral shape, irradiance, direction, and diffuseness of daylight vary regularly throughout the day. The variations in illumination and their effect on the light reflected from objects may in turn provide visual information as to the time of day. We suggest that artists' color choices for paintings of outdoor scenes might convey this information and that therefore the time of day might be decoded from the colors of paintings. Here we investigate whether human viewers' estimates of the depicted time of day in paintings correlate with their image statistics, specifically chromaticity and luminance variations. We tested time-of-day perception in 17th- to 20th-century Western European paintings via two online rating experiments. In Experiment 1, viewers' ratings from seven time choices varied significantly and largely consistently across paintings but with some ambiguity between morning and evening depictions. Analysis of the relationship between image statistics and ratings revealed correlations with the perceived time of day: higher "morningness" ratings associated with higher brightness, contrast, and saturation and darker yellow/brighter blue hues; "eveningness" with lower brightness, contrast, and saturation and darker blue/brighter yellow hues. Multiple linear regressions of extracted principal components yielded a predictive model that explained 76% of the variance in time-of-day perception. In Experiment 2, viewers rated paintings as morning or evening only; rating distributions differed significantly across paintings, and image statistics predicted people's perceptions. These results suggest that artists used different color palettes and patterns to depict different times of day, and the human visual system holds consistent assumptions about the variation of natural light depicted in paintings.

Effect of pre‐receptor filters on chromatic sensitivity and other visual functions in the eye

Spatial and spectral information carried by light emitted from visual displays is filtered as it passes through spectacle lenses in front of the eye as well as by filters within the eye. The image forming and the spectral transmittance properties of the elements involved in image formation in the eye cause changes in both the luminance and colour contrast in retinal images. In addition, the amount of light that reaches the retina is always reduced by pre‐receptor filters and this reduction in retinal illuminance can cause diminished retinal sensitivity to both luminance and colour contrast. Of particular interest are the use of coloured lenses in front of the eye to reduce the short‐wavelength content of the light that reaches the retina and the use of ‘notch’ filters to absorb light selectively in certain wavebands, with inevitable consequences on both the luminance and chromatic contrast of retinal images. The presence of variant cone‐pigments in some subjects or the complete absence of one class of cone pigment in others, can also produce large deviations in both luminance and colour contrast signals that can either enhance or diminish the conspicuity of coloured objects (doi: 10.1364/josaa.22.000017).The purpose of this study was to develop and validate a model of red/green (RG) and yellow/blue (YB) colour discrimination that can also predict how chromatic sensitivity is affected by the use of spectrally‐selective, spectacle lenses or by filters internal to the eye, when viewing visual displays. We examined how RG and YB colour thresholds relate to the corresponding cone contrasts needed for detection of colour differences. The results reveal an invariance of cone contrasts at threshold for different states of chromatic adaptation. In other words, colour contrast signals at threshold follow Weber's law (doi: 10.1111/j.1475‐1313.2010.00773.x). A combination of RG and YB colour signals is needed to predict the colour threshold ellipse. The model relies on these findings and predicts how coloured filters external to the eye as well as pre‐receptor filters within the eye alter the measured RG and YB colour thresholds. Model predictions have been compared against measured colour thresholds using the CAD test (doi: 10.1093/bmb/ldx007). A number of experiments were carried out when the visual display was viewed with no lens in front of the eye or through a number of tinted lenses designed to absorb progressively more blue light.The effects of pre‐receptor filters within the eye such as the lens and the macular pigment have also been investigated. The results show that ‘blue‐blocking’ filters employed in typical spectacle lenses used with daylight illumination cause only small shifts, approximately along the daylight locus, without any significant loss of either RG or YB chromatic sensitivity. ‘Orange’ lenses that absorb greater amounts of short‐wavelength light cause a significant increase in YB thresholds, but have little or no effect on RG thresholds. More surprisingly, the model also predicts that the optical density of the macular pigment has little or no effect on the size of the colour threshold ellipse.Keywords: colour, cone contrasts, blue light, CAD test.

Unsupervised Low-Light Image Enhancement in the Fourier Transform Domain

Low-light image enhancement is an important task in computer vision. Deep learning-based low-light image enhancement has made significant progress. But the current methods also face the challenge of relying on a wide variety of low-light/normal-light paired images and amplifying noise while enhancing brightness. Based on existing experimental observation that most luminance information concentrates on amplitudes while noise is closely related to phases, an unsupervised low-light image enhancement method in the Fourier transform domain is proposed. In our method, the low-light image is firstly transformed into the amplitude component and phase component via Fourier transform. The luminance of low-light image is enhanced by CycleGAN in the amplitude domain, and the phase component is denoising. The cycle consistency losses both in the Fourier transform domain and spatial domain are used in training. The proposed method has been validated on publicly available test sets and shows that our method achieves superior results than other approaches in low-light image enhancement and noise suppression.

Low-illumination image enhancement with logarithmic tone mapping

Abstract For low-illumination video sequences, some existing enhancement algorithms have some problems, such as image over-enhancement, color distortion, and inadequate detail processing. Based on luminance detection, we add logarithmic tone mapping to optimize the existing algorithms. The color space of low-illumination video image is converted from the red, green, blue mode to the hue-saturation-intensity mode, and then, logarithmic tone enhancement is applied to the image. Algorithm in this study has an obvious effect on image luminance enhancement and details processing, which makes the low-illumination video show a clear image with more natural visual effect, thus improving the quality of low-illumination video. This algorithm can avoid the problems of overexposure, color distortion, and blurring of detail processing under low illumination. The infrared spectrum of the object can be captured by infrared detection equipment, and the purpose of image enhancement can be achieved by applying the infrared spectrum of the object.

MEGF-Net: multi-exposure generation and fusion network for vehicle detection under dim light conditions

Vehicle detection in dim light has always been a challenging task. In addition to the unavoidable noise, the uneven spatial distribution of light and dark due to vehicle lights and street lamps can further make the problem more difficult. Conventional image enhancement methods may produce over smoothing or over exposure problems, causing irreversible information loss to the vehicle targets to be subsequently detected. Therefore, we propose a multi-exposure generation and fusion network. In the multi-exposure generation network, we employ a single gated convolutional recurrent network with two-stream progressive exposure input to generate intermediate images with gradually increasing exposure, which are provided to the multi-exposure fusion network after a spatial attention mechanism. Then, a pre-trained vehicle detection model in normal light is used as the basis of the fusion network, and the two models are connected using the convolutional kernel channel dimension expansion technique. This allows the fusion module to provide vehicle detection information, which can be used to guide the generation network to fine-tune the parameters and thus complete end-to-end enhancement and training. By coupling the two parts, we can achieve detail interaction and feature fusion under different lighting conditions. Our experimental results demonstrate that our proposed method is better than the state-of-the-art detection methods after image luminance enhancement on the ODDS dataset.

The combination of cognitive psychology and creative thinking skills in art and design

Abstract This paper first investigates cognitive psychology and art design, divides cognitive memory into sensory memory, short-term memory and long-term memory according to cognitive psychology, and investigates art design's needs, psychology and creative thinking ability. Then, an art design is proposed that uses image processing technology. The first step in designing an art design using image processing technology is to use an image processing algorithm. Based on the image luminance equalization processing in art design, image fusion and wavelet noise reduction processing are carried out, and ANOVA cognitive psychology and art design experimental data are used to realize art design human-computer interaction. The results show that the analysis of the model with artistic design creative thinking variability as the dependent variable found that the R2 of the model was 0.036, and the adjusted R2 was 0.023. F=2.633, p=0.015, and the grade level had a significant effect on artistic design creative thinking variability. This study is a comprehensive and thorough cognitive psychology and art and design relationship that can contribute to development.

SAGAN: Deep semantic-aware generative adversarial network for unsupervised image enhancement

Low-light image enhancement (LLIE) is a common pretext task for computer vision, which aims to adjust the luminance of the low-light image to obtain the normal-light image. At present, unsupervised LLIE has been developed. However, its performance is limited due to the lack of sufficient semantic information and guidance from a strict discriminator. In this work, a semantic-aware generative adversarial network is proposed to alleviate the above limitations. We use the pre-trained VGG model on ImageNet to extract the prior semantic information, which is organically fed into the generator to refine its feature representation, and develop an adaptive image fusion strategy working on the output layer of the generator. Further, to improve the discriminator’s capacity of supervising generator, we design the dual-discriminator with dense connection and two image quality-driven priority queues with time-aware. The quantitative and qualitative experiments on four testing datasets demonstrate the competitiveness of the proposed model and the effectiveness of each component. Our code is available at: https://github.com/Shecyy/SAGAN.

Weighted side-window based gradient guided image filtering

Image filtering under guidance image, known as guided filtering (GF), has been successfully applied to a variety of applications. Existing GF methods utilize either conventional full window-based framework (FWF) or simple uniformly weighted aggregation strategy (UWA); thereby they suffer from edge-blurring. In this paper, based upon gradient guided filtering (GGF), a weighted side-window based gradient guided filtering (WSGGF) is proposed to address the aforementioned problem. First, both regression and adaptive regularization terms in GGF are improved upon eight side windows by introducing side window-based framework (SWF). L1 norm is adopted to choose the results calculated in side windows. Second, UWA strategy in GGF is replaced by a refined variance-based weighted average (VWA) aggregation. In VWA, the value of each weight is chosen inversely proportional to the corresponding estimator. We show that with these improvements our method can well retain the edge sharpness and is robust to visual artifacts. To cut down the time consumption, a fast version of WSGGF (FWSGGF) is further proposed by incorporating a simple but effective down-sampling strategy, which is about four times faster while maintaining the superior performance. By comparing with the state-of-the-art (SOTA) methods on edge-aware smoothing, detail enhancement, high dynamic range image (HDR) compression, image luminance adjustment, depth map upsampling and single image haze removal, the effectiveness and flexibility of our proposed methods are verified. The source code is available at: https://github.com/weimin581/WSGGF