Showing papers on "Contrast (vision) published in 2020"
TL;DR: The authors show that neurons in the auditory thalamus and midbrain of mice display independent contrast gain control, not just the cortex as previously thought, and that this is implemented independently of cortical activity.
Abstract: Neural adaptation enables sensory information to be represented optimally in the brain despite large fluctuations over time in the statistics of the environment. Auditory contrast gain control represents an important example, which is thought to arise primarily from cortical processing. Here we show that neurons in the auditory thalamus and midbrain of mice show robust contrast gain control, and that this is implemented independently of cortical activity. Although neurons at each level exhibit contrast gain control to similar degrees, adaptation time constants become longer at later stages of the processing hierarchy, resulting in progressively more stable representations. We also show that auditory discrimination thresholds in human listeners compensate for changes in contrast, and that the strength of this perceptual adaptation can be predicted from physiological measurements. Contrast adaptation is therefore a robust property of both the subcortical and cortical auditory system and accounts for the short-term adaptability of perceptual judgments. Auditory contrast gain control helps us perceive sounds as constant despite changes in the environment or background noise. Here, the authors show that neurons in the auditory thalamus and midbrain of mice display independent contrast gain control, not just the cortex as previously thought.
48 citations
TL;DR: A novel small target detection method via multidirectional derivative-based weighted contrast measure (MDWCM) is proposed and demonstrates that the proposed algorithm performs favorably compared to other state-of-the-art methods.
Abstract: Infrared (IR) small target detection in complex backgrounds is one of the key technologies in IR search and tracking applications. Although significant progress has been made over the past few decades, how to separate a small target from complex backgrounds remains a challenging task. In this letter, a novel small target detection method via multidirectional derivative-based weighted contrast measure (MDWCM) is proposed. Initially, multidirectional derivative subbands are quickly obtained by the facet model. Then, an effective division scheme of surrounding area is performed to capture the derivative properties of the target. A new local contrast measure is constructed to simultaneously enhance the target and suppress the background clutter. Third, the MDWCM maps constructed from all derivative subbands are integrated to enhance the robustness of detection. Finally, the small target is extracted by an adaptive segmentation method. The experimental results demonstrate that the proposed algorithm performs favorably compared to other state-of-the-art methods.
47 citations
TL;DR: A computational luminance-dependent model is presented that predicts the CSF for achromatic and chromatic stimuli of arbitrary size and finds that the background luminance has a differential effect on achromatics contrast sensitivity compared to chromatic contrast sensitivity.
Abstract: Contrast sensitivity functions (CSFs) characterize the sensitivity of the human visual system at different spatial scales, but little is known as to how contrast sensitivity for achromatic and chromatic stimuli changes from a mesopic to a highly photopic range reflecting outdoor illumination levels. The purpose of our study was to further characterize the CSF by measuring both achromatic and chromatic sensitivities for background luminance levels from 0.02 cd/m2 to 7,000 cd/m2. Stimuli consisted of Gabor patches of different spatial frequencies and angular sizes, varying from 0.125 to 6 cpd, which were displayed on a custom high dynamic range (HDR) display with luminance levels up to 15,000 cd/m2. Contrast sensitivity was measured in three directions in color space, an achromatic direction, an isoluminant "red-green" direction, and an S-cone isolating "yellow-violet" direction, selected to isolate the luminance, L/M-cone opponent, and S-cone opponent pathways, respectively, of the early postreceptoral processing stages. Within each session, observers were fully adapted to the fixed background luminance (0.02, 2, 20, 200, 2,000, or 7,000 cd/m2). Our main finding is that the background luminance has a differential effect on achromatic contrast sensitivity compared to chromatic contrast sensitivity. The achromatic contrast sensitivity increases with higher background luminance up to 200 cd/m2 and then shows a sharp decline when background luminance is increased further. In contrast, the chromatic sensitivity curves do not show a significant sensitivity drop at higher luminance levels. We present a computational luminance-dependent model that predicts the CSF for achromatic and chromatic stimuli of arbitrary size.
35 citations
TL;DR: It is shown that 'conscious' visual discrimination abilities are often preserved inside subacute, perimetrically-defined blind fields, but they disappear by ∼6 months post-stroke, which suggests that after V1 damage, rather than waiting for vision to stabilize, early training interventions may be key to maximize the system's potential for recovery.
Abstract: Stroke damage to the primary visual cortex (V1) causes a loss of vision known as hemianopia or cortically-induced blindness. While perimetric visual field improvements can occur spontaneously in the first few months post-stroke, by 6 months post-stroke, the deficit is considered chronic and permanent. Despite evidence from sensorimotor stroke showing that early injury responses heighten neuroplastic potential, to date, visual rehabilitation research has focused on patients with chronic cortically-induced blindness. Consequently, little is known about the functional properties of the post-stroke visual system in the subacute period, nor do we know if these properties can be harnessed to enhance visual recovery. Here, for the first time, we show that 'conscious' visual discrimination abilities are often preserved inside subacute, perimetrically-defined blind fields, but they disappear by ∼6 months post-stroke. Complementing this discovery, we now show that training initiated subacutely can recover global motion discrimination and integration, as well as luminance detection perimetry, just as it does in chronic cortically-induced blindness. However, subacute recovery was attained six times faster; it also generalized to deeper, untrained regions of the blind field, and to other (untrained) aspects of motion perception, preventing their degradation upon reaching the chronic period. In contrast, untrained subacutes exhibited spontaneous improvements in luminance detection perimetry, but spontaneous recovery of motion discriminations was never observed. Thus, in cortically-induced blindness, the early post-stroke period appears characterized by gradual-rather than sudden-loss of visual processing. Subacute training stops this degradation, and is far more efficient at eliciting recovery than identical training in the chronic period. Finally, spontaneous visual improvements in subacutes were restricted to luminance detection; discrimination abilities only recovered following deliberate training. Our findings suggest that after V1 damage, rather than waiting for vision to stabilize, early training interventions may be key to maximize the system's potential for recovery.
33 citations
TL;DR: A normalised gamma transformation-based contrast-limited adaptive histogram equalisation (CLAHE) with colour correction in Lab colour space for sand-dust image enhancement is proposed in this study and can obtain the highest percentage of new visible edges for all testing images.
Abstract: Images captured in the sand-dust weather often suffer from serious colour cast and poor contrast, and this has serious implications for outdoor computer vision systems. To address these problems, a normalised gamma transformation-based contrast-limited adaptive histogram equalisation (CLAHE) with colour correction in Lab colour space for sand-dust image enhancement is proposed in this study. This method consists of image contrast enhancement and image colour correction. To avoid producing new colour deviation, the input sand-dust images are first transformed from red, green, and blue colour space into Lab colour space. Then, the contrast of the lightness component (L channel) of the sand-dust image is enhanced using CLAHE. To avoid unbalanced contrast, as well as to reduce the overincreased brightness caused by CLAHE, a normalised gamma correction function is introduced to CLAHE. After that, the a and b chromatic components are recovered by a grey-world-based colour correction method. Experiments on real sand-dust images demonstrate that the proposed method can obtain the highest percentage of new visible edges for all testing images. The contrast restoration exhibits good colour fidelity and proper brightness.
32 citations
TL;DR: Simulations show that contrast adaptation can substantially improve motion estimates in natural scenes, and the benefits are larger for ON-pathway adaptation, which helps explain the heterogeneous distribution of contrast adaptation in these circuits.
31 citations
TL;DR: In this article, the authors demonstrate dynamic contrast with a scanning frequency-domain OCT (FD-OCT), which is a promising tool for the histological analysis of unstained tissues.
Abstract: While optical coherence tomography (OCT) provides a resolution down to 1 µm, it has difficulties in visualizing cellular structures due to a lack of scattering contrast. By evaluating signal fluctuations, a significant contrast enhancement was demonstrated using time-domain full-field OCT (FF-OCT), which makes cellular and subcellular structures visible. The putative cause of the dynamic OCT signal is the site-dependent active motion of cellular structures in a sub-micrometer range, which provides histology-like contrast. Here we demonstrate dynamic contrast with a scanning frequency-domain OCT (FD-OCT), which we believe has crucial advantages. Given the inherent sectional imaging geometry, scanning FD-OCT provides depth-resolved images across tissue layers, a perspective known from histopathology, much faster and more efficiently than FF-OCT. Both shorter acquisition times and tomographic depth-sectioning reduce the sensitivity of dynamic contrast for bulk tissue motion artifacts and simplify their correction in post-processing. Dynamic contrast makes microscopic FD-OCT a promising tool for the histological analysis of unstained tissues.
30 citations
TL;DR: The results indicate that the proposed method can effectively improve the clarity of a retinal image without introducing a considerable color difference.
29 citations
TL;DR: Quantitative and qualitative results demonstrate that the proposed method produces an enhanced image with superior perceptual quality, and gives the best average results for all the parameters across every dataset as compared to the state-of-the-art methods.
Abstract: A novel white balancing algorithm is proposed in this paper to automatically enhance the global contrast degraded imperceptible images The technique is applied on four publicly available image dataset, CSIQ, KADID, TID and SIPI Colour images consist of three channels viz Red, Blue and Green A contrast degraded colour image visually appears similar to an image with one or more distorted channel 12 images are obtained by enhancing one channel of the contrast degraded image at the cost of other channel using White Balancing algorithm Four images with best quantitative performance metrics, visual similarity index (VSI), gradient magnitude similarity index (GMSD), patch-based contrast quality index (PCQI) and peak signal-to-noise ratio (PSNR) determines the pair of weak and prominent channels An optimization algorithm then enhances these channels and the image with the best quantitative performance metrics is chosen as the enhanced image Quantitative and qualitative results demonstrate that the proposed method produces an enhanced image with superior perceptual quality, and gives the best average results for all the parameters across every dataset as compared to the state-of-the-art methods
25 citations
TL;DR: It is found that distributed representations in the visual system can nonetheless support specialized perceptual roles for higher visual cortical areas, consistent with a role for these areas in sensory perception.
24 citations
TL;DR: The two proposed constructions for constituting a threshold probabilistic CBW-VCS are introduced, where the generated color shares are non-expansible, and are proven to be valid constructions which satisfy the security and contrast conditions.
TL;DR: The results show that visual performance can be improved by increasing the CCT of the light sources, and the improvement of visual performance is greater in peripheral vision than that in foveal vision.
TL;DR: The visual performance obtained with bilateral implantation of the trifocal aspheric AcrySofIQ PanOptix IOL is good at far, intermediate, and near distances, and the binocular distance contrast sensitivity was within normal limits.
Abstract: Purpose To evaluate distance, intermediate, and near visual performance in patients implanted with a trifocal aspheric presbyopia-correcting intraocular lens (IOL). Methods Forty patients were bilaterally implanted with the AcrySofIQ PanOptix IOL after femtosecond laser-assisted lens surgery. Binocular best corrected distance visual acuity (CDVA) (4 m), best distance-corrected near visual acuity (DCNVA) (40 and 30 cm), best corrected distance intermediate visual acuity (DCIVA) (70, 60, and 50 cm), binocular distance contrast sensitivity under photopic conditions (85 cd/m2), and defocus curves were evaluated at 6-months. Results Six months postoperatively, the mean binocular Snellen decimal CDVA and DCNVA were 0.94 ± 0.10 (ranging from 0.70 to 1.25) and 0.85 ± 0.13 (ranging from 0.63 to 1.00), respectively. At a distance, all patients showed a cumulative binocular distance-corrected visual acuity of 0.8 or better, and about 80% (n = 31) of the patients had a value of 1.0 (20/20). At near and intermediate distances, all patients showed a cumulative distance-corrected visual acuity of 0.5 (20/40) or better at 30, 40, 50, 60, and 70 cm. Specifically, 50 cm showed the highest percentage of patients with larger values of visual acuity (60% [n = 26] with 20/20). Defocus curve showed a wide range of useful vision with two peaks of best visual acuity at distance and at 50 cm, and the binocular distance contrast sensitivity was within normal limits. Conclusions The outcomes of the present study show that the visual performance obtained with bilateral implantation of the trifocal aspheric AcrySofIQ PanOptix IOL is good at far, intermediate, and near distances.
TL;DR: The physics of black‐blood contrast and different techniques to achieve blood suppression are covered, from methods intrinsic to the imaging readout to magnetization preparation pulses that can be combined with arbitrary readouts, including flow‐dependent and flow‐independent techniques.
Abstract: MRI is a versatile technique that offers many different options for tissue contrast, including suppressing the blood signal, so-called black-blood contrast. This contrast mechanism is extremely useful to visualize the vessel wall with high conspicuity or for characterization of tissue adjacent to the blood pool. In this review we cover the physics of black-blood contrast and different techniques to achieve blood suppression, from methods intrinsic to the imaging readout to magnetization preparation pulses that can be combined with arbitrary readouts, including flow-dependent and flow-independent techniques. We emphasize the technical challenges of black-blood contrast that can depend on flow and motion conditions, additional contrast weighting mechanisms (T1 , T2 , etc.), magnetic properties of the tissue, and spatial coverage. Finally, we describe specific implementations of black-blood contrast for different vascular beds. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 5.
TL;DR: A modified semantic segmentation networks (CNNs) based method has been proposed for both MRI and CT images and attains better results when compared to existing methods.
Abstract: Segmentation of brain tumor is one of the crucial tasks in medical image process. So as to boost the treatment prospects and to extend the survival rate of the patients, early diagnosing of brain tumors imagined to be a crucial role. Magnetic Resonance Imaging (MRI) is a most widely used diagnosis method for tumors. Also current researches are intended to improve the MRI diagnosis by adding contrast agents as contrast enhanced MRI provides accurate details about the tumors. Computed Tomography (CT) images also provide the internal structure of the organs. The manual segmentation of tumor depends on the involvement of radiotherapist and their expertise. It may cause some errors due to the massive volume of MRI (Magnetic Resonance Imaging) data. It is very difficult and time overwhelming task. This created the environment for automatic brain tumor segmentation. Currently, machine learning techniques play an essential role in medical imaging analysis. Recently, a very versatile machine learning approach called deep learning has emerged as an upsetting technology to reinforce the performance of existing machine learning techniques. In this work, a modified semantic segmentation networks (CNNs) based method has been proposed for both MRI and CT images. Classification also employed in the proposed work. In the proposed architecture brain images are first segmented using semantic segmentation network which contains series of convolution layers and pooling layers. Then the tumor is classified into three different categories such as meningioma, glioma and pituitary tumor using GoogLeNet CNN model. The proposed work attains better results when compared to existing methods.
TL;DR: A new image dehazing method for remote sensing (RS) applications that focuses on degraded objects, including color correction and color–contrast enhancement, and shows that the color, contrast, naturalness, and high brightness of the object increase in the image to be improved.
Abstract: The presence of suspended particles, such as fog, smoke, and dust, in the atmosphere reduces the quality of the captured image. It is essential to overcome these particles, because these particles have a very dire effect on different applications of image processing. We propose a new image dehazing method for remote sensing (RS) applications. Since the hazy RS image is affected by multiple colors and contrast reduction, our goal is to focus on degraded objects, including color correction and color–contrast enhancement. A “Piecewise Linear Transformation (PWLT)” is used to correct the color distortion, and then the color contrast is improved by applying the proposed method. The intensity distribution manipulation is one of the most common methods used to strengthen image contrast in the past. Compared with advanced techniques, the proposed method is easy to implement and suitable for real-time applications. Moreover, it is not necessary to require prior imaging conditions. The experimental results show that, in terms of subjective and visual quality, the color, contrast, naturalness, and high brightness of the object increase in the image to be improved.
TL;DR: A new no-reference/blind image quality assessment (IQA) metric is proposed for evaluating image contrast, and extensive analysis and cross validation are performed, which validates the superiority of the proposed blind technique over state-of-the-art no- reference IQA methods.
TL;DR: It is concluded that the chromatic signals from LCA are dependent on the relative amount of S-cone temporal modulation, and recommend broadband spectral and temporal environments, such as the outdoor environment, to optimize the signals-for-defocus in chick.
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TL;DR: The Narrowest Significance Pursuit enables the opposite viewpoint and paves the way for the concept of "post-inference selection", and its key computational component uses simple linear programming.
Abstract: We propose Narrowest Significance Pursuit (NSP), a general and flexible methodology for automatically detecting localised regions in data sequences which each must contain a change-point, at a prescribed global significance level. Here, change-points are understood as abrupt changes in the parameters of an underlying linear model. NSP works by fitting the postulated linear model over many regions of the data, using a certain multiresolution sup-norm loss, and identifying the shortest interval on which the linearity is significantly violated. The procedure then continues recursively to the left and to the right until no further intervals of significance can be found. The use of the multiresolution sup-norm loss is a key feature of NSP, as it enables the transfer of significance considerations to the domain of the unobserved true residuals, a substantial simplification. It also guarantees important stochastic bounds which directly yield exact desired coverage probabilities, regardless of the form or number of the regressors.
NSP works with a wide range of distributional assumptions on the errors, including Gaussian with known or unknown variance, some light-tailed distributions, and some heavy-tailed, possibly heterogeneous distributions via self-normalisation. It also works in the presence of autoregression. The mathematics of NSP is, by construction, uncomplicated, and its key computational component uses simple linear programming. In contrast to the widely studied "post-selection inference" approach, NSP enables the opposite viewpoint and paves the way for the concept of "post-inference selection". Pre-CRAN R code implementing NSP is available at this https URL.
TL;DR: A definition for the term Foveated Display is recommended and a taxonomy is provided to allow the field to meaningfully compare and contrast various aspects of foveated displays in a display and optical technology-agnostic manner.
Abstract: Emergent in the field of head mounted display design is a desire to leverage the limitations of the human visual system to reduce the computation, communication, and display workload in power and form-factor constrained systems. Fundamental to this reduced workload is the ability to match display resolution to the acuity of the human visual system, along with a resulting need to follow the gaze of the eye as it moves, a process referred to as foveation . A display that moves its content along with the eye may be called a Foveated Display , though this term is also commonly used to describe displays with non-uniform resolution that attempt to mimic human visual acuity. We therefore recommend a definition for the term Foveated Display that accepts both of these interpretations. Furthermore, we include a simplified model for human visual Acuity Distribution Functions (ADFs) at various levels of visual acuity, across wide fields of view and propose comparison of this ADF with the Resolution Distribution Function of a foveated display for evaluation of its resolution at a particular gaze direction. We also provide a taxonomy to allow the field to meaningfully compare and contrast various aspects of foveated displays in a display and optical technology-agnostic manner.
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TL;DR: A single-stage keypoint-based network, named as FADNet, is presented to address the task of monocular 3D object detection, which proposes to divide the output modalities into different groups according to the estimating difficulty, whereby different groups are treated differently by sequential feature association.
Abstract: Monocular 3D object detection is a promising research topic for the intelligent perception systems of autonomous driving. In this work, a single-stage keypoint-based network, named as FADNet, is presented to address the task of monocular 3D object detection. In contrast to previous keypoint-based methods which adopt identical layouts for output branches, we propose to divide the output modalities into different groups according to the estimating difficulty, whereby different groups are treated differently by sequential feature association. Another contribution of this work is the strategy of depth hint augmentation. To provide characterized depth patterns as hints for depth estimation, a dedicated depth hint module is designed to generate row-wise features named as depth hints, which are explicitly supervised in a bin-wise manner. In the training stage, the regression outputs are uniformly encoded to enable loss disentanglement. The 2D loss term is further adapted to be depth-aware for improving the detection accuracy of small objects. The contributions of this work are validated by conducting experiments and ablation study on the KITTI benchmark. Without utilizing depth priors, post optimization, or other refinement modules, our network performs competitively against state-of-the-art methods while maintaining a decent running speed.
TL;DR: The pathology-specific dissociation between VA and CS is investigated by quantifying and comparing the relationship between these two measures in common ocular pathologies and CS appears to provide valuable complementary information in the early detection of eye disease and when evaluating visual impairment.
Abstract: Author(s): Xiong, Ying-Zi; Kwon, MiYoung; Bittner, Ava K; Virgili, Gianni; Giacomelli, Giovanni; Legge, Gordon E
TL;DR: It is found that domestic chicks were less likely to approach and eat prey with high contrast compared to low contrast patterns, suggesting that aposematic prey patterns with a high luminance contrast can benefit from increased survival through eliciting unlearned biases in naïve avian predators.
Abstract: An apparent and common feature of aposematic patterns is that they contain a high level of achromatic (luminance) contrast, for example, many warning signals combine black spots and stripes with a lighter colour such as yellow. However, the potential importance of achromatic contrast, as distinct from colour contrast, in reducing predation has been largely overlooked. Here, using domestic chicks as a model predator, we manipulated the degree of achromatic contrast in warning patterns to test if high luminance contrast in aposematic signals is important for deterring naive predators. We found that the chicks were less likely to approach and eat prey with high contrast compared to low contrast patterns. These findings suggest that aposematic prey patterns with a high luminance contrast can benefit from increased survival through eliciting unlearned biases in naive avian predators. Our work also highlights the importance of considering luminance contrast in future work investigating why aposematic patterns take the particular forms that they do.
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TL;DR: Short acquisition times and tomographic depth-sectioning reduce the sensitivity of dynamic contrast for bulk tissue motion artifacts and simplify their correction in post-processing, which makes microscopic FD-OCT a promising tool for the histological analysis of unstained tissues.
Abstract: While optical coherence tomography (OCT) provides a resolution down to 1 micrometer it has difficulties to visualize cellular structures due to a lack of scattering contrast. By evaluating signal fluctuations, a significant contrast enhancement was demonstrated using time-domain full-field OCT (FF-OCT), which makes cellular and subcellular structures visible. The putative cause of the dynamic OCT signal is ATP-dependent motion of cellular structures in a sub-micrometer range, which provides histology-like contrast. Here we demonstrate dynamic contrast with a scanning frequency-domain OCT (FD-OCT). Given the inherent sectional imaging geometry, scanning FD-OCT provides depth-resolved images across tissue layers, a perspective known from histopathology, much faster and more efficiently than FF-OCT. Both, shorter acquisition times and tomographic depth-sectioning reduce the sensitivity of dynamic contrast for bulk tissue motion artifacts and simplify their correction in post-processing. The implementation of dynamic contrast makes microscopic FD-OCT a promising tool for histological analysis of unstained tissues.
TL;DR: Results of the experimentation show that the proposed technique enhance the image contrast up to a good degree while preserving the image details.
Abstract: The low contrast medical images seriously affect the clinical diagnosis process. To improve the image quality, we propose an effective medical images contrast enhancement technique in this paper. Shear wavelet transformation is used for decomposition of image components into low-frequency
and high-frequency. The low-frequency part contrast is adjusted by applying modified contrast limited adaptive histogram equalization (CLAHE). The resultant image is further processed through technique of fuzzy contrast enhancement to maintain the spectral information of an image. Results
of the experimentation show that our proposed technique enhance the image contrast up to a good degree while preserving the image details.
TL;DR: The proposed Tone-mapping algorithm is compared with state-of-the-art algorithms, using some well-known metrics that quantify the quality of tone-mapped images, and is found to have the best performance.
Abstract: A new tone-mapping algorithm is presented for visualization of high dynamic range (HDR) images on low dynamic range (LDR) displays. In the first step, the real-world pixel intensities of the HDR image are transformed to a perceptual domain using the perceptual-quantizer (PQ). This is followed by construction of the histogram of the luminance channel. Tone-mapping curve is generated from the cumulative histogram. It is known that histogram-based tone-mapping approaches can lead to excessive stretching of contrast in highly populated bins, whereas the pixels in sparse bins can suffer from excessive compression of contrast. We handle these issues by restricting the pixel counts in the histogram to remain below a defined limit, determined by a uniform distribution model. The proposed method is compared with state-of-the-art algorithms, using some well-known metrics that quantify the quality of tone-mapped images, and is found to have the best performance.
TL;DR: A model that accounted for both retinal illuminance and age allowed VSX, termed VSX(td,a), to best track visual acuity trends (measured at 160 and 200 cd/m2) as a function of age from the literature.
Abstract: Visual image quality metrics combine comprehensive descriptions of ocular optics (from wavefront error) with a measure of the neural processing of the visual system (neural contrast sensitivity). To improve the ability of these metrics to track real-world changes in visual performance and to investigate the roles and interactions of those optical and neural components in foveal visual image quality as functions of age and target luminance, models of neural contrast sensitivity were constructed from the literature as functions of (1) retinal illuminance (Trolands, td), and (2) retinal illuminance and age. These models were then incorporated into calculation of the visual Strehl ratio (VSX). Best-corrected VSX values were determined at physiological pupil sizes over target luminances of 104 to 10-3 cd/m2 for 146 eyes spanning six decades of age. Optical and neural components of the metrics interact and contribute to visual image quality in three ways. At target luminances resulting in >900 td at physiological pupil size, neural processing is constant, and only aberrations (that change as pupil size changes with luminance) affect the metric. At low mesopic luminances below where pupil size asymptotes to maximum, optics are constant (maximum pupil), and only the neural component changes with luminance. Between these two levels, both optical and neural components of the metrics are affected by changes in target luminance. The model that accounted for both retinal illuminance and age allowed VSX, termed VSX(td,a), to best track visual acuity trends (measured at 160 and 200 cd/m2) as a function of age (20s through 70s) from the literature. Best-corrected VSX(td,a) decreased by 2.24 log units between maximum and minimum target luminances in the youngest eyes and by 2.58 log units in the oldest. The decrease due to age was more gradual at high target luminances (0.70 log units) and more pronounced as target luminance decreased (1.04 log units).
TL;DR: Even with good central visual acuity, patients with glaucomatous macular damage exhibit diminished facial recognition, which is partly mediated through diminished CS.
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TL;DR: The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining it with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in Contrast and brightness when the contrast and brightness of the reference are not good.
Abstract: Low light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low light image enhancement method that can combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low light images as input and the enhanced V channel as condition, then it can re-enhance the contrast and brightness of the low light image and at the same time reduce noise and color distortion. It should be noted that during the training process, any paired images with different exposure time can be used for training, and there is no need to carefully select the supervised images which will save a lot. In addition, it takes less than 20 ms to process a color image with the resolution 400*600 on a 2080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness. (Code will be available at this https URL)
TL;DR: The proposed contrast stretching and intensity transfer are main steps in both of the two solutions that can effectively improve the contrast and illuminance and good visual perception for quality degraded retinal images is obtained.
Abstract: Proper contrast and sufficient illuminance are important in clearly identifying the retinal structures, while the required quality cannot always be guaranteed due to major reasons like acquisition process and diseases. To ensure the effectiveness of enhancement, two solutions are developed for blurry retinal images with sufficient illuminance and insufficient illuminance, respectively. The proposed contrast stretching and intensity transfer are main steps in both of the two solutions. The contrast stretching is based on base-intensity removal and non-uniform addition. We assume that a base-intensity exists in an image, which mainly supports the basic illuminance but has less contribution to texture information. The base-intensity is estimated by the constrained Gaussian function and then removed. The non-uniform addition using compressed Gamma map is further developed to improve the contrast. Additionally, an effective intensity transfer strategy is introduced, which can provide required illuminance for a single channel after contrast stretching. The color correction can be achieved if the intensity transfer is performed on three channels. Results show that the proposed solutions can effectively improve the contrast and illuminance, and good visual perception for quality degraded retinal images is obtained. Illustration of contrast stretching based on a signal colour channel.