Color Enhancement of Low Illumination Garden Landscape Images

The unfavorable shooting environment severely hinders the acquisition of actual landscape information in garden landscape design. Low quality, low illumination garden landscape images (GLIs) can be enhanced through advanced digital image processing. However, the current color enhancement models have poor applicability. When the environment changes, these models are easy to lose image details, and perform with a low robustness. Therefore, this paper tries to enhance the color of low illumination GLIs. Specifically, the color restoration of GLIs was realized based on modified dynamic threshold. After color correction, the low illumination GLI were restored and enhanced by a self-designed convolutional neural network (CNN). In this way, the authors achieved ideal effects of color restoration and clarity enhancement, while solving the difficulty of manual feature design in landscape design renderings. Finally, experiments were carried out to verify the feasibility and effectiveness of the proposed image color enhancement approach.

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior

In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications.

Drivers’ Risk Perception and Risky Driving Behavior under Low Illumination Conditions: Modified Driver Behavior Questionnaire (DBQ) and Driver Skill Inventory (DSI)

Most road crashes are caused by human factors. Risky behaviors and lack of driving skills are two human factors that contribute to crashes. Considering the existing evidence, risky driving behaviors and driving skills have been regarded as potential decisive factors explaining and preventing crashes. Nighttime accidents are relatively frequent and serious compared with daytime accidents. Therefore, it is important to focus on driving behaviors and skills to reduce traffic accidents and enhance safe driving in low illumination conditions. In this paper, we examined the relation between drivers’ risk perception and propensity for risky driving behavior and conducted a comparative analysis of the associations between risk perception, propensity for risky driving behavior, and other factors in the presence and absence of streetlights. Participants in Hefei city, China, were asked to complete a demographic questionnaire, the Driver Behavior Questionnaire (DBQ), and the Driver Skill Inventory (DSI). Multiple linear regression analyses identified some predictors of driver behavior. The results indicated that both the DBQ and DSI are valuable instruments in traffic safety analysis in low illumination conditions and indicated that errors, lapses, and risk perception were significantly different between with and without streetlight conditions. Pearson’s correlation test found that elderly and experienced drivers had a lower likelihood of risky driving behaviors when driving in low illumination conditions, and crash involvement was positively related to risky driving behaviors. Regarding the relationship between study variables and driving skills, the research suggested that age, driving experience, and annual distance were positively associated with driving skills, while myopia, penalty points, and driving self-assessment were negatively related to driving skills. Furthermore, the differences across age groups in errors, lapses, violations, and risk perception in the presence of streetlights were remarkable, and the driving performance of drivers aged 45–55 years was superior to that of drivers in other age groups. Finally, multiple linear regression analyses showed that education background and crash involvement had a positive influence on error, whereas risk perception had a negative effect on errors; crash involvement had a positive influence, while risk perception had a negative effect on lapse; driving experience and crash involvement had a positive influence on violation; and age had a negative influence on it.

Induced electronic phenomena in crystals of p-GaSe semiconductor promising for optoelectronics

An induced impurity photoconductivity by the electric field, thermally stimulated conductivity and spontaneous pulsations of the dark current were found in the undoped (with a dark resistivity P77≈3•104÷108 Ω-cm at T≈77 K) and erbium doped (NEr=10–5÷10–1 at.%) p-GaSe crystals in the temperature range of T≤240÷250 K at electric field strengths (E) creating a noticeable injection. It was found that the value of the observed impurity photoconductivity (M) monotonically increase at low illumination in undoped crystals with increasing P77 and its spectrum smoothly expands towards longer waves. The value of ∆ii and the width of its spectrum change non-monotonically with increasing NEr in doped crystal and it gets its maximum value at NEr ≈5•10-4 at.%. The intensity of spontaneous pulsations increases with increasing E at the higher electric field strengths. However, the impurity photoconductivity and the peak of thermally stimulated conductivity gradually disappeared. The amplitude and frequency of the observed spontaneous pulsations of the dark current is increased with increasing in the injection ability of the contacts. Moreover, the pulsations of the dark current gradually disappeared with increasing T. It was shown that all these three phenomena are directly caused by the recharge of sticking levels with a depth Er ≈+0.42 eV and a density Nt≈ 1015 cm-3 by injected holes. However, in high-resistance undoped and doped Er ≤10-2 at.% crystals, it is also necessary to consider the presence of random macroscopic defects in the samples to explain their features. A qualitative explanation is proposed based on the obtained results.

Digital Video Image Preprocessing Algorithm Based on Embedded System

To solve the massive noise contained in the images acquired under low illumination, we designed a digital video image Preprocessing device with the denoising function. Based on the embedded CPU and operating system, video images are acquired by the camera. The noise contained in the video images is filtered by the improved median filtering algorithm and wavelet image denoising. Subsequently, the images are transmitted through USB and network interface, and the storage function of image files is implemented. The device can remove the noise contained in videos effectively, which is conducive to performing more advanced processing on the images.

Low illumination image enhancement based on multi-scale CycleGAN with deep residual shrinkage

Low-illumination image restoration has been widely used in many fields. Aiming at the problem of low resolution and noise amplification in low light environment, this paper applies style transfer of CycleGAN(Cycle-Consistent Generative Adversarial Networks) to low illumination image enhancement. In the design network structure, different convolution kernels are used to extract the features from three paths, and the deep residual shrinkage network is designed to suppress the noise after convolution. The color deviation of the image can be resolved by the identity loss of CycleGAN. In the discriminator, different convolution kernels are used to extract image features from two paths. Compared with the training and testing results of Deep-Retinex network, GLAD network, KinD and other network methods on LOL-dataset and Brightening dataset, CycleGAN based on multi-scale depth residuals contraction proposed in this experiment on LOL-dataset results image quality evaluation indicators PSNR = 24.62, NIQE = 4.9856, SSIM = 0.8628, PSNR = 27.85, NIQE = 4.7652, SSIM = 0.8753. From the visual effect and objective index, it is proved that CycleGAN based on multi-scale depth residual shrinkage has excellent performance in low illumination enhancement, detail recovery and denoising.

To the question of the influence of experimental inflammation on the content of K, Ca and water in the skin of rabbits

E. Nathan and F. Stern (Dermat. Ztschr., Bd. 55, H. 1), as a result of intense illumination of the skin of rabbits with ultraviolet rays, an increased content of Ca was observed in the areas of inflamed skin, the amount of water at the beginning of inflammation increased, by the end of inflammation it decreased. By the end of the inflammation, the K content in these areas increased under low illumination with the same rays. The content of Ca and water was the same as in the case of intense irradiation, but an increase in the content of K was noted already from the onset of inflammation. The changes in the skin of rabbits after irritation with mustard oil were in general the same as when they were irradiated with light.