Scene Contrast Research Articles

Visible, NIR, SWIR, and eSWIR urban and rural scene contrast comparison.

The spectral information contained in the reflective imaging bands can be exploited for specific tasks. Whether targeting or mapping, the visible (VIS), near-infrared (NIR), shortwave infrared (SWIR), extended shortwave infrared (eSWIR) bands perform very differently for every application. For any imaging project, high contrast is very important for good imagery. High contrast leads to more recognizable features within a scene and easier identifiable objects. For mapping, good background scene contrast gives prominent features more detail and their locations can be easily identified. For targeting, low background scene contrast reduces clutter, making it easier to detect objects of interest. The VIS, NIR, SWIR, and eSWIR bands are popular reflective bands to design daytime imaging systems for either task. Deciding on which band will have the best contrast for a specific task is one of the first things to study when designing an imaging system. By measuring urban and rural scenes in terms of equivalent reflectivity (ER), a direct comparison of these four bands can show the utility they provide. The systems used to measure scene contrast are designed to have the same spatial resolution and field of view (FOV). With these instantaneous FOV (IFOV) matched systems, the variance and 1D power spectral densities (PSDs) provide a quantitative comparison for the contrast among the four bands. The ER differences and resulting contrast measured among these four bands show that the eSWIR has the highest contrast in both urban and rural scenes.

Visual assessment of fashion merchandising based on scene saliency

PurposeFashion merchandising hold a paramount position within the realm of retail marketing. Currently, the purpose of this article is that the assessment of display effectiveness predominantly relies on the subjective judgment of merchandisers due to the absence of an effective evaluation method. Although eye-tracking devices have found extensive used in tracking the gaze trajectory of subject, they exhibit limitations in terms of stability when applied to the evaluation of various scenes. This underscores the need for a dependable, user-friendly and objective assessment method.Design/methodology/approachTo develop a cost-effective and convenient evaluation method, the authors introduced an image processing framework for the assessment of variations in the impact of store furnishings. An optimized visual saliency methodology that leverages a multiscale pyramid model, incorporating color, brightness and orientation features, to construct a visual saliency heatmap. Additionally, the authors have established two pivotal evaluation indices aimed at quantifying attention coverage and dispersion. Specifically, bottom features are extract from 9 distinct scale images which are down sampled from merchandising photographs. Subsequently, these extracted features are amalgamated to form a heatmap, serving as the focal point of the evaluation process. The authors have proposed evaluation indices dedicated to measuring visual focus and dispersion, facilitating a precise quantification of attention distribution within the observed scenes.FindingsIn comparison to conventional saliency algorithm, the optimization method yields more intuitive feedback regarding scene contrast. Moreover, the optimized approach results in a more concentrated focus within the central region of the visual field, a pattern in alignment with physiological research findings. The results affirm that the two defined indicators prove highly effective in discerning variations in visual attention across diverse brand store displays.Originality/valueThe study introduces an intelligent and cost-effective objective evaluate method founded upon visual saliency. This pioneering approach not only effectively discerns the efficacy of merchandising efforts but also holds the potential for extension to the assessment of fashion advertisements, home design and website aesthetics.

FOLD: Low-Level Image Enhancement for Low-Light Object Detection Based on FPGA MPSoC

Object detection has a wide range of applications as the most fundamental and challenging task in computer vision. However, the image quality problems such as low brightness, low contrast, and high noise in low-light scenes cause significant degradation of object detection performance. To address this, this paper focuses on object detection algorithms in low-light scenarios, carries out exploration and research from the aspects of low-light image enhancement and object detection, and proposes low-level image enhancement for low-light object detection based on the FPGA MPSoC method. On the one hand, the low-light dataset is expanded and the YOLOv3 object detection model is trained based on the low-order image enhancement technique, which improves the detection performance of the model in low-light scenarios; on the other hand, the model is deployed on the MPSoC board to achieve an edge object detection system, which improves the detection efficiency. Finally, validation experiments are conducted on the publicly available low-light object detection dataset and the ZU3EG-AXU3EGB MPSoC board, and the results show that the method in this paper can effectively improve the detection accuracy and efficiency.

Comparison of scene contrast temperature in mid-wave infrared and long-wave infrared

Comparison of scene contrast temperature in mid-wave infrared and long-wave infrared

An efficient image defogging algorithm based on dragonfly optimized gamma correction and stationary wavelet decomposition

Image dehazing is a revolutionary technique for restoring images with hazy or foggy landscapes, that has gotten a lot of focus in recent years since it gained importance in a surveillance system. However, the image processing by the traditional defogging algorithm has difficulties in integrating the depth of image detail and the color of the image. Therefore, in this paper, a novel framework based on wavelet decomposition and optimized gamma correction is proposed for efficaciously retrieving the fog-free image. The foggy image is first divided into low and high frequency sub-images using SWT (Stationary Wavelet Transform), which has the advantages of preserving temporal features so that information loss can be stopped. Then the low frequency and high frequency images are processed with defogging and denoising modules to remove fog and noise respectively. The DOGC (Dragonfly optimal Gamma Correction) algorithm in dehazing module dynamically enhanced the color detail information without human intervention so that observed scene contrast and visibility are well preserved. Lastly, fog-free image is reconstructed from sub-enhanced images. The experimental findings show that the proposed framework outperforms state-of-the-art methods in terms of both quantitative and qualitative assessment criteria using the established dataset. Furthermore, the proposed method efficiently removes fog while preserving the naturalness of fog images.

Fortresses as Ideological Images of Power

This paper is a discussion of the essential, indeed the key, element of New Kingdom pictorial representations of warfare in Asia surrounding the depiction of the enemy citadel-fortress. We appear to find this depiction from the early stages of the 18th Dynasty onwards, but its inclusion as well as absence needs further specification, and that is the aim of this study. Based on earlier analyses of images of warfare, the following contribution carries the vector of imagery and historical development. The contrast of these urban scenes with Libyan and Nubian combat depictions, frequently touched upon in scholarship, will be partially limned in this paper. [Formula: see text]

Contrast and Luminance Gain Control in the Macaque's Lateral Geniculate Nucleus.

There is substantial variation in the mean and variance of light levels (luminance and contrast) in natural visual scenes. Retinal ganglion cells maintain their sensitivity despite this variation using two adaptive mechanisms, which control how responses depend on luminance and on contrast. However, the nature of each mechanism and their interactions downstream of the retina are unknown. We recorded neurons in the magnocellular and parvocellular layers of the lateral geniculate nucleus (LGN) in anesthetized adult male macaques and characterized how their responses adapt to changes in contrast and luminance. As contrast increases, neurons in the magnocellular layers maintain sensitivity to high temporal frequency stimuli but attenuate sensitivity to low-temporal frequency stimuli. Neurons in the parvocellular layers do not adapt to changes in contrast. As luminance increases, both magnocellular and parvocellular cells increase their sensitivity to high-temporal frequency stimuli. Adaptation to luminance is independent of adaptation to contrast, as previously reported for LGN neurons in the cat. Our results are similar to those previously reported for macaque retinal ganglion cells, suggesting that adaptation to luminance and contrast result from two independent mechanisms that are retinal in origin.

Editorial Issue 34.2

Editorial Issue 34.2

Three-Stage Tone Mapping Algorithm

In this paper, a tone mapping algorithm is presented to map real-world luminance into displayed luminance. Our purpose is to reveal the local contrast of real-world scenes on a conventional monitor. Around this point, we propose a three-stage algorithm to visualize high dynamic range images. All pixels of high dynamic range images are classified into three groups. For the first stage, we introduce piecewise linear mapping as the global tone mapping operator to map the luminance of the first group, which provides overall impressions of luminance. For the second stage, the luminance of the second group is determined by the weighted average of its neighborhood pixels, which are derived from the first group’s pixels. For the third stage, the luminance of the third group is determined by the weighted average of its neighborhood pixels, which are derived from the second group’s pixels. Experimental results on several real-world images and the TMQI database show that our algorithm can improve the visibility of real-world scenes with about 12% and 9% higher scores of mean opinion score and tone-mapped image quality index than the closest competitive tone mapping methods. Compared to the existing tone mapping methods, our algorithm produces visually compelling results without halo artifacts and loss of detail.

Integrating deep learning and traditional image enhancement techniques for underwater image enhancement

Underwater images usually suffer from colour distortion, blur, and low contrast, which hinder the subsequent processing of underwater information. To address these problems, this paper proposes a novel approach for single underwater images enhancement by integrating data-driven deep learning and hand-crafted image enhancement techniques. First, a statistical analysis is made on the average deviation of each channel of input underwater images to that of its corresponding ground truths, and it is found that both the red channel and the green channel of an underwater image contribute to its colour distortion. Concretely, the red channel of an underwater image is usually seriously attenuated, and the green channel is usually over strengthened. Motivated by such an observation, an attention mechanism guided residual module for underwater image colour correction is proposed, where the colour of the red channel of the underwater image and that of the green channel is compensated in a different way, respectively. Coupled with an attention mechanism, the residual module can adaptively extract and integrate the most discriminative features for colour correction. For scene contrast enhancement and scene deblurring, the traditional image enhancement techniques such as CLAHE (contrast limited adaptive histogram equalization) and Gamma correction are coupled with a multi-scale convolutional neural network (MSCNN), where CLAHE and Gamma correction are used as complement to deal with the complex and changeable underwater imaging environment. Experiments on synthetic and real underwater images demonstrate that the proposed method performs favourably against the state-of-the-art underwater image enhancement methods.

The impact of locomotion techniques on user experience on virtual reality on light of different scene contrasts

AbstractLocomotion techniques are an important part of the virtual reality (VR) experience, as they enable a means of movement within simulated computer graphics. Studies have shown that locomotion techniques cause unpleasant effects related to cybersickness. In this article, we report on the results of a user study (N = 29) aimed to investigate and compare three types of locomotion techniques in VR in terms of their impact on cybersickness. The different locomotion techniques are tested in two different contrast scene settings, daytime and nighttime, to further explore the potential impact of scene contrast adjustments. For the evaluation of cybersickness, we used a questionnaire to obtain subjective ratings, and heart rate monitoring as an objective metric. Results show that a linear movement locomotion technique provokes the highest level of cybersickness, and that women have a higher heart rate as compared to men when navigating and interacting in a VR scene. Regarding the influence of scene contrast, results showed that scenes with daylight were better suited to participants in almost all tested scenarios. In addition to reported findings related to locomotion techniques and the impact on cybersickness, we highlight that a key contribution is the utilized test methodology.

Rapid nighttime haze removal with color-gray layer decomposition

Nighttime haze usually reduces the visible distance and visual contrast of the nighttime scene. To resolve this problem, a color-gray layer decomposition method is presented to decompose a real-world image into color-world and gray-world layers in the RGB color space. Based on the presented layer decomposition, a new nighttime dehazing framework is proposed to remove glowing color, enhance illumination, and remove haze. Specifically, to correct nighttime hazy images, an effective glow decomposition approach is present to remove glowing color in the color-world layer. For low-light nighttime hazy scenes, gamma correction is applied to enhance illumination in the gray-world layer. After nighttime hazy image correction, to further refine the coarse estimation of the transmission and the atmospheric light, a relative weighted guided image filter is devised with a new relative edge-aware weighting. Additionally, a 2D atmospheric scattering model is established to well remove haze with the decomposed transmission and the fused local atmospheric light. Therefore, the proposed method can rapidly recover high-quality nighttime dehazing results and preserve the intrinsic color of the scene while avoiding color artifacts. Quantitative comparisons verify that the proposed nighttime dehazing framework can achieve excellent performance in nighttime haze removal against previous state-of-the-art methods.

A fibre-based 2D-slit homogenizer concept for high-precision space-based spectrometer missions

The measurement accuracy of recent and future space-based imaging spectrometers with a high spectral and spatial resolution suffer from the inhomogeneity of the radiances of the observed Earth scene. The Instrument Spectral Response Function (ISRF) is distorted due to the inhomogeneous illumination from scene heterogeneity. This gives rise to a pseudo-random error on the measured spectra. In order to assess the spectral stability of the spectrograph, stringent requirements are typically defined on the ISRF such as shape knowledge and the stability of the centroid position of the spectral sample. The high level of spectral accuracy is particularly crucial for missions quantifying small variations in the total column of well-mixed trace gases like hbox {CO}_{2}. In the framework of the hbox {CO}_{2} Monitoring Mission (CO2M) industrial feasibility study (Phase A/B1 study), we investigated a new slit design called 2D-Slit Homogenizer (2DSH). This new concept aims to reduce the Earth scene contrast entering the instrument. The 2DSH is based on optical fibre waveguides assembled in a bundle, which scramble the light in across-track (ACT) and along-track (ALT) direction. A single fibre core dimension in ALT defines the spectral extent of the slit and the dimension in ACT represents the spatial sample of the instrument. The full swath is given by the total size of the adjoined fibres in ACT direction. In this work, we provide experimental measurement data on the stability of representative rectangular core shaped fibre as well as a preliminary pre-development of a 2DSH fibre bundle. In our study, the slit concept has demonstrated significant performance gains in the stability of the ISRF for several extreme high-contrast Earth scenes, achieving a shape stability of <0.5{%} and a centroid stability of <0.25 text {pm} (NIR). Given this unprecedented ISRF stabilization, we conclude that the 2DSH concept efficiently desensitizes the instrument for radiometric and spectral errors with respect to the heterogeneity of the Earth scene radiance.

Background Modeling Combined With Multiple Features in the Fourier Domain for Maritime Infrared Target Detection

To detect ship targets in dynamic sea backgrounds, a robust foreground detection method based on background modeling combined with multiple features in the Fourier domain (BMMFF) is proposed. Because the fluctuation of seawater is similar to a sine wave, the amplitude spectrum of the seawater in the Fourier domain has strong energy concentration, and the background model is built in the Fourier domain. The local statistical characteristics can effectively reflect the properties of a specific position, and the target frequency points are extracted by comparing the local statistical characteristic differences between the amplitude spectrum of the test frame and the updated background. Additionally, two different strategies are used to update the background for cases with or without sudden waves in two adjacent test frames. When the target and seawater have different contrast in different scenes, the threshold is set according to the seawater's fluctuation degree, which is classified into three different levels in the training stage. Moreover, the linear correlation feature between the test frame and the updated background and the oscillation feature of the test frame's amplitude spectrum are proposed in the Fourier domain, which can better segment targets in relatively calm and violent sea scenes, respectively. Thus, the two features are combined with the background model with a reasonable strategy to further improve the detection accuracy. The experimental results demonstrate that BMMFF outperforms related comparison algorithms in different challenging sea scenes. The average false alarm rate of BMMFF is reduced by approximately 30% in the case where its average detection rate is similar to that of other algorithms.

Tone mapping high dynamic range images based on region-adaptive self-supervised deep learning

This paper presents a region-adaptive self-supervised deep learning (RASSDL) technique for high dynamic range (HDR) image tone mapping. The RASSDL tone mapping operator (TMO) is a convolutional neural network (CNN) trained on local image regions that can seamlessly tone map images of arbitrary sizes. The training of RASSDL TMO is through the design of a self-supervising target that automatically adapts to the local image regions based on their information contents. The self-supervising target is designed to ensure the tone-mapped output achieves a balance between preserving the relative contrast of the original scene and the visibilities of the fine details to achieve faithful reproduction of the HDR scene. Distinguishing from many existing TMOs that require manual tuning of parameters, RASSDL is parameter-free and completely automatic. Experimental results demonstrate that RASSDL TMO can achieve state-of-the-art performance in terms of preserving overall contrasts, revealing fine details, and being free from visual artifacts.

Slit homogenizer introduced performance gain analysis based on the Sentinel-5/UVNS spectrometer

Abstract. Spatially heterogeneous Earth radiance scenes affect the atmospheric composition measurements of high-resolution Earth observation spectrometer missions. The scene heterogeneity creates a pseudo-random deformation of the instrument spectral response function (ISRF). The ISRF is the direct link between the forward radiative transfer model, used to retrieve the atmospheric state, and the spectra measured by the instrument. Hence, distortions of the ISRF owing to radiometric inhomogeneity of the imaged Earth scene will degrade the precision of the Level-2 retrievals. Therefore, the spectral requirements of an instrument are often parameterized in the knowledge of the ISRF over non-uniform scenes in terms of shape, centroid position of the spectral channel and the full width at half maximum (FWHM). The Sentinel-5/UVNS instrument is the first push-broom spectrometer that makes use of a concept referred to as a slit homogenizer (SH) for the mitigation of spatially non-uniform scenes. This is done by employing a spectrometer slit formed by two parallel mirrors scrambling the scene in the along track direction (ALT) and hence averaging the scene contrast only in the spectral direction. The flat mirrors do not affect imaging in the across track direction (ACT) and thus preserve the spatial information in that direction. The multiple reflections inside the SH act as coherent virtual light sources and the resulting interference pattern at the SH exit plane can be described by simulations using scalar diffraction theory. By homogenizing the slit illumination, the SH strongly modifies the spectrograph pupil illumination as a function of the input scene. In this work we investigate the impact and strength of the variations of the spectrograph pupil illumination for different scene cases and quantify the impact on the ISRF stability for different types of aberration present in the spectrograph optics.

Image and Video Dehazing based on Gamma Correction Method Contrast Enhancement

As the revolution in computational photography and computer vision applications facilitates fast and reliable information, quality of the scene and visual perception and is being increasingly used in various fields like public safety, traffic accident analysis, crime forensics, remote sensing area and military surveillance. We make an investigation of the dehazing effect of scenes affected by weather phenomena. ‘Dehazing’ has emerged as a promising technology to recover the clear image and video from an input hazy scene, such that the quality can be significantly enhanced. A scene captured in the outdoor environment affected by haze like fog mist and dust particles in the atmosphere. We are utilizing a ‘Dehazing algorithm, to remove this unwanted haze from videos and Real-time video. Also, remove haze from images, for this, we use a novel method of video dehazing based on contrast enhancement. From our observation, it is concluded that hazy image and video has low contrast, so we estimate transmission map to maximize the contrast of output scene. And we use the depth estimation process to detect or identify hidden parameters from the scene, and also creates a corresponding haze scene with high fidelity. Finally, we reconstruct or restore the seen without changing its originality. Hence dehazing performance with fewer artifacts and better coding efficiency and demonstrate that the proposed algorithm can remove haze efficiently and recover the parameters of the original scene

Image and Video Dehazing based on Gamma Correction Method Contrast Enhancement

As the revolution in computational photography and computer vision applications facilitates fast and reliable information, quality of the scene and visual perception and is being increasingly used in various fields like public safety, traffic accident analysis, crime forensics, remote sensing area and military surveillance. We make an investigation of the dehazing effect of scenes affected by weather phenomena. ‘Dehazing’ has emerged as a promising technology to recover the clear image and video from an input hazy scene, such that the quality can be significantly enhanced. A scene captured in the outdoor environment affected by haze like fog mist and dust particles in the atmosphere. We are utilizing a ‘Dehazing algorithm, to remove this unwanted haze from videos and Real-time video. Also, remove haze from images, for this, we use a novel method of video dehazing based on contrast enhancement. From our observation, it is concluded that hazy image and video has low contrast, so we estimate transmission map to maximize the contrast of output scene. And we use the depth estimation process to detect or identify hidden parameters from the scene, and also creates a corresponding haze scene with high fidelity. Finally, we reconstruct or restore the seen without changing its originality. Hence dehazing performance with fewer artifacts and better coding efficiency and demonstrate that the proposed algorithm can remove haze efficiently and recover the parameters of the original scene.

Spatial receptive field structure of double-opponent cells in macaque V1.

The spatial processing of color is important for visual perception. Double-opponent (DO) cells likely contribute to this processing by virtue of their spatially opponent and cone-opponent receptive fields (RFs). However, the representation of visual features by DO cells in the primary visual cortex of primates is unclear because the spatial structure of their RFs has not been fully characterized. To fill this gap, we mapped the RFs of DO cells in awake macaques with colorful, dynamic white noise patterns. The spatial RF of each neuron was fitted with a Gabor function and three versions of the difference of Gaussians (DoG) function. The Gabor function provided the more accurate description for most DO cells, a result that is incompatible with a center-surround RF organization. A nonconcentric version of the DoG function, in which the RFs have a circular center and a crescent-shaped surround, performed nearly as well as the Gabor model thus reconciling results from previous reports. For comparison, we also measured the RFs of simple cells. We found that the superiority of the Gabor fits over DoG fits was slightly more decisive for simple cells than for DO cells. The implications of these results on biological image processing and visual perception are discussed.NEW & NOTEWORTHY Double-opponent cells in macaque area V1 respond to spatial chromatic contrast in visual scenes. What information they carry is debated because their receptive field organization has not been characterized thoroughly. Using white noise analysis and statistical model comparisons, De and Horwitz show that many double-opponent receptive fields can be captured by either a Gabor model or a center-with-an-asymmetric-surround model but not by a difference of Gaussians model.

Low-level image statistics in natural scenes influence perceptual decision-making

A fundamental component of interacting with our environment is gathering and interpretation of sensory information. When investigating how perceptual information influences decision-making, most researchers have relied on manipulated or unnatural information as perceptual input, resulting in findings that may not generalize to real-world scenes. Unlike simplified, artificial stimuli, real-world scenes contain low-level regularities that are informative about the structural complexity, which the brain could exploit. In this study, participants performed an animal detection task on low, medium or high complexity scenes as determined by two biologically plausible natural scene statistics, contrast energy (CE) or spatial coherence (SC). In experiment 1, stimuli were sampled such that CE and SC both influenced scene complexity. Diffusion modelling showed that the speed of information processing was affected by low-level scene complexity. Experiment 2a/b refined these observations by showing how isolated manipulation of SC resulted in weaker but comparable effects, with an additional change in response boundary, whereas manipulation of only CE had no effect. Overall, performance was best for scenes with intermediate complexity. Our systematic definition quantifies how natural scene complexity interacts with decision-making. We speculate that CE and SC serve as an indication to adjust perceptual decision-making based on the complexity of the input.