Existing learning-based methods for low-light image enhancement contain a large number of redundant features, the enhanced images lack detail and have strong noises. Some methods try to combine the ...pyramid structure to learn features from coarse to fine, but the inconsistency of the pyramid structure leads to luminance, color and texture deviations in the enhanced images. In addition, these methods are usually computationally complex and require high computational resource requirements. In this paper, we propose an efficient adaptive feature aggregation network (EAANet) for low-light image enhancement. Our model adopts a pyramid structure and includes multiple multi-scale feature aggregation block (MFAB) and one adaptive feature aggregation block (AFAB). MFAB is proposed to be embedded into each layer of the pyramid structure to fully extract features and reduce redundant features, while the AFAB is proposed for overcome the inconsistency of the pyramid structure. EAANet is very lightweight, with low device requirements and a quick running time. We conducted an extensive comparison with some state-of-the-art methods in terms of PSNR, SSIM, parameters, computations and running time on LOL and MIT5K datasets, and the experiments show that the proposed method has significant advantages in terms of comprehensive performance. The proposed method reconstructs images with richer color and texture, and the noises is effectively suppressed.
This paper proposes a new method for low-light image enhancement with balancing image brightness and preserving image details, this method can improve the brightness and contrast of low-light images ...while maintaining image details. Traditional histogram equalization methods often lead to excessive enhancement and loss of details, thereby resulting in an unclear and unnatural appearance. In this method, the image is processed bidirectionally. On the one hand, the image is processed by double histogram equalization with double automatic platform method based on improved cuckoo search (CS) algorithm, where the image histogram is segmented firstly, and the platform limit is selected according to the histogram statistics and improved CS technology. Then, the sub-histograms are clipped by two platforms and carried out the histogram equalization respectively. Finally, an image with balanced brightness and good contrast can be obtained. On the other hand, the main structure of the image is extracted based on the total variation model, and the image mask with all the texture details is made by removing the main structure of the image. Eventually, the final enhanced image is obtained by adding the mask with texture details to the image with balanced brightness and good contrast. Compared with the existing methods, the proposed algorithm significantly enhances the visual effect of the low-light images, based on human subjective evaluation and objective evaluation indices. Experimental results show that the proposed method in this paper is better than the existing methods.
Despite powerful advances in yield curve modeling in the last 20 years, comparatively little attention has been paid to the key practical problem of forecasting the yield curve. In this paper we do ...so. We use neither the no-arbitrage approach nor the equilibrium approach. Instead, we use variations on the Nelson–Siegel exponential components framework to model the entire yield curve, period-by-period, as a three-dimensional parameter evolving dynamically. We show that the three time-varying parameters may be interpreted as factors corresponding to level, slope and curvature, and that they may be estimated with high efficiency. We propose and estimate autoregressive models for the factors, and we show that our models are consistent with a variety of stylized facts regarding the yield curve. We use our models to produce term-structure forecasts at both short and long horizons, with encouraging results. In particular, our forecasts appear much more accurate at long horizons than various standard benchmark forecasts.
This paper proposes a new method for low-light image enhancement with balancing image brightness and preserving image details, this method can improve the brightness and contrast of low-light images ...while maintaining image details. Traditional histogram equalization methods often lead to excessive enhancement and loss of details, thereby resulting in an unclear and unnatural appearance. In this method, the image is processed bidirectionally. On the one hand, the image is processed by double histogram equalization with double automatic platform method based on improved cuckoo search (CS) algorithm, where the image histogram is segmented firstly, and the platform limit is selected according to the histogram statistics and improved CS technology. Then, the sub-histograms are clipped by two platforms and carried out the histogram equalization respectively. Finally, an image with balanced brightness and good contrast can be obtained. On the other hand, the main structure of the image is extracted based on the total variation model, and the image mask with all the texture details is made by removing the main structure of the image. Eventually, the final enhanced image is obtained by adding the mask with texture details to the image with balanced brightness and good contrast. Compared with the existing methods, the proposed algorithm significantly enhances the visual effect of the low-light images, based on human subjective evaluation and objective evaluation indices. Experimental results show that the proposed method in this paper is better than the existing methods.
Lung diseases have become a major threat to human health worldwide. Despite advances in treatment and intervention in recent years, effective drugs are still lacking for many lung diseases. As a ...traditional natural medicine, Tibetan medicine has had a long history of medicinal use in ethnic minority areas, and from ancient times to the present, it has a good effect on the treatment of lung diseases and has attracted more and more attention. In this review, a total of 586 Tibetan medicines were compiled through literature research of 25 classical works on Tibetan medicine, drug standards, and some Chinese and English databases. Among them, 33 Tibetan medicines have been studied to show their effectiveness in treating lung diseases. To investigate the uses of these Tibetan medicines in greater depth, we have reviewed the ethnomedicinal, phytochemical and pharmacological properties of the four commonly used Tibetan medicines for lung diseases (rhodiola, gentian, sea buckthorn, liexiang dujuan) and the five most frequently used Tibetan medicines (safflower, licorice, sandalwood, costus, myrobalan). It is expected to provide some reference for the development of new drugs of lung diseases in the future.
To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. ...However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the state-of-the-art LLIE methods. Keywords: low-light image enhancement, deep learning, edge detection, multi-scale feature enhancement, spatial attention mechanism.
Lane detection is an important and challenging part of autonomous driver assistance systems and other advanced assistance systems. The presence of road potholes and obstacles, complex road ...environments (illumination, occlusion, etc.) are ubiquitous, will cause the blur of images, which is captured by the vision perception system in the lane detection task. To improve the lane detection accuracy of blurred images, a network (Lane-GAN) for lane line detection is proposed in the paper, which is robust to blurred images. First, real and complex blur kernels are simulated to construct a blurred image dataset, and the improved GAN network is used to reinforce the lane features of the blurred image, and finally the feature information is further enriched with a recurrent feature transfer aggregator. Extensive experimental results demonstrate that the proposed network can get robust detection results in complex environments, especially for blurred lane lines. Compared with the SOTA detector, the proposed detector achieves a larger gain. The proposed method can enhance the lane detail features of the blurred image, improving the detection accuracy of the blurred lane effectively, in the driver assistance system in high speed and complex road conditions.
The popular Nelson–Siegel Nelson, C.R., Siegel, A.F., 1987. Parsimonious modeling of yield curves. Journal of Business 60, 473–489 yield curve is routinely fit to cross sections of intra-country bond ...yields, and Diebold–Li Diebold, F.X., Li, C., 2006. Forecasting the term structure of government bond yields. Journal of Econometrics 130, 337–364 have recently proposed a dynamized version. In this paper we extend Diebold–Li to a global context, modeling a potentially large
set of country yield curves in a framework that allows for both global and country-specific factors. In an empirical analysis of term structures of government bond yields for the Germany, Japan, the UK and the US, we find that global yield factors do indeed exist and are economically important, generally explaining significant fractions of country yield curve dynamics, with interesting differences across countries.
How to ensure the safety of abandoned mine resources, scientifically develop and utilize abandoned mine resources, and promote the transformation of resource-exhausted mining areas have become an ...important issue in the field of energy and environment in the world today. Aiming at the stability of the surrounding rock in deep closed/abandoned mine chamber, the mechanical model of the surrounding rock under the coupling effect of anchorage and seepage field was proposed. Considering the elastic brittleness degradation and plastic dilatancy effect of rock mass, the analytical solutions of stress and displacement of rockbolt-seepage-surrounding rock coupling system were respectively deduced, and the accuracy of the results were verified. Based on the analytical results, the evolution law of stress and displacement of the surrounding rock under the combined action of seepage field and anchorage effect were further revealed, and a new quantitative design method of rockbolt parameters was proposed. Results show that the influence of rockbolt spacing and rod diameter on the mechanical field is obvious, while the rockbolt length and pre-tension load is small. Dense, short rockbolt with larger diameter should be used in the surrounding rock of deep chamber. The influence of seepage on the displacement of the surrounding rock is very significant. The more serious the seepage is, the more obvious the control effect of rockbolt on the displacement is. Appropriately increasing the density and diameter of rockbolt can effectively reduce the displacement of the surrounding rock.
Article Highlights
Closed analytical solutions of stress and displacement in deep buried abandoned chamber under the coupling effect of anchorage-seepage are respectively derived.
The influence of rockbolt parameters and seepage field on the stress and displacement field of the surrounding rock is revealed.
A new quantitative design method of rockbolt parameters is proposed.
Aiming at solving the problems of overall darkness, uneven illumination and low contrast of image under low illumination conditions, we present a global and adaptive contrast enhancement algorithm ...for low illumination gray images in this paper. The proposed algorithm is based on the Bilateral Gamma Adjustment function and combined with the particle swarm optimization (PSO). For the PSO, the gray standard variance is integrated into the evaluation function. To reconcile the dilemma of promoting the gray values of dark areas and suppressing the gray values of local bright areas at the same time, the information of entropy, edge content, and gray standard variance are used as the objective function for each particle to evaluate the gray image enhancement results. Then, the algorithm globally enhances the quality of the image by determining the optimal \alpha value. Meanwhile, the learning factors of the PSO are updated during the iteration of optimization in the proposed algorithm. Compared with histogram equalization (HE), double plateau histogram equalization (DPHE), contrast limited adaptive histogram equalization (CLAHE), linear contrast stretching (LCS), adaptive gamma correction weighting distribution (AGCWD), the traditional PSO and MSF-PSO algorithm, the proposed algorithm significantly enhances the visual effect of the low illumination gray images. The experimental results demonstrate the superior capabilities of the proposed algorithm in enhancing the contrast of the image, such as improving the overall visual effect of the low illumination gray image and avoiding over-enhancement in the local area (s).
PDF
