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.

A High-Performance Infrared Imaging System with Adaptive Contrast Enhancement

Most of the existing infrared imaging systems employ the scheme of FPGA/FPGA+DSP with numerous peripheral circuits, which leads to complex hardware architecture, limited system versatility, and low computing performance. It has become an intriguing technical problem worldwide to simplify the system structure while improving the imaging performance. In this paper, we present a novel real-time infrared imaging system based on the Rockchip’s RV1108 visual processing SoC (system on chip). Moreover, to address the problem of low contrast and dim details in infrared images with a high dynamic range, an adaptive contrast enhancement method based on bilateral filter is proposed and implemented on the system. First, the infrared image is divided into a base layer and a detail layer through bilateral filter, then the base layer is compressed by an adaptive bi-plateau histogram equalization algorithm, and finally a linear-weighted method is used to integrate the detail layer to obtain the image with enhanced details. The experimental results indicate that compared with traditional algorithms, our method can effectively improve the overall contrast of the image, while effectively retaining the image details without noise magnification. For an image of 320*240 pixels, the real-time processing rate of the system is 68 frames/s. The system has the characteristics of simplified structure, perceptive image details, and high computing performance.

Protocol for applying Machine Learning models for the transformation of conventional fluorescence images to super-resolution

Machine Learning offers the opportunity to visualize the invisible in conventional fluorescence microscopy images by improving their resolution while preserving and enhancing image details. This protocol describes the application of GAN-based Machine Learning models to transform the resolution of conventional fluorescence microscopy images to a resolution comparable with super-resolution. It provides a flexible environment using a modern app functioning on both desktop and mobile computers. This approach can be extended for use on other types of microscopy images empowering life science researchers with modern analytical tools.

Modified method of image histogram hyperbolization

One of the methods to improve image quality, which consists in increasing the resolution of image details by contrast enhancement, is to hyperbolize the image histogram. Herewith this increase in local contrast is carried out indirectly. It is due to the nature of the change in the histogram of the transformed image. Usually the histogram of the input image is transformed so that it has a uniform distribution, which illustrates the same contribution of pixels gray level to the image structure. However, there is a method that is based on modeling the human visual system, which is characterized by the logarithmic dependence of the human reaction to light stimulation. It consists in the hyperbolic transformation of the histogram of the image. Then, due to its perception by the visual system, at its output, during the psychophysical perception of the image, an approximately uniform distribution of the histogram of the levels of gray pixels is formed. But the drawback is the lack of effectiveness of this approach for excessively light or dark images. The modified method of image histogram hyperbolization has been developed. It is based on the power transformation of the probability distribution function, which in the discrete version of the images is approximated by a normalized cumulative histogram. The power index is a control parameter of the transformation. to improve the darkened images we use the value of the control parameter less than one, and for light images more than one. The effectiveness of the proposed method is shown by examples.

Multistage Feature Complimentary Network for Single-Image Deraining

Rain will cause the occlusion and blur of background and target objects and affect the image visual effect and subsequent image analysis. Aiming at the problem of insufficient rain removal in the current rain removal algorithm, in order to improve the accuracy of computer vision algorithm in the process of rain removal, this paper proposes a multistage framework based on progressive restoration combined with recurrent neural network and feature complementarity technology to remove rain streak from single images. Firstly, the encoder-decoder subnetwork is adapted to learn multiscale information and extract richer rain features. Secondly, the original resolution image restored by decoder is used to preserve refined image details. Finally, we use the effective information of the previous stage to guide the rain removal of the next stage by the recurrent neural network. The final experimental results show that a multistage feature complementarity network performs well on both synthetic rainy data sets and real-world rainy data sets can remove rain more completely, preserve more background details, and achieve better visual effects compared with some popular single-image deraining methods.

The method of increasing the information content of microfocus X-ray images

A method for processing microfocus X-ray images is described. It is based on high-frequency filtration and morphological image processing, which increases the contrast of the X-ray details. One of the most informative X-ray techniques is microfocus X-ray. In some cases, microfocus X-ray images cannot be reliably analyzed due to the peculiarities of the shooting method. So, the main disadvantages of microfocus X-ray images are most often an uneven background, distorted brightness characteristics and the presence of noise. The proposed method for enhancing the contrast of fine image details is based on the idea of combining high-frequency filtering and morphological image processing. The method consists of the following steps: noise suppression in the image, high-frequency filtering, morphological image processing, obtaining the resulting image. As a result of applying the method, the brightness of the contours in the image is enhanced. In the resulting image, all objects will have double outlines. The method was tested in the processing of 50 chest radiographs of patients with various pathologies. Radiographs were performed at the Mariinsky Hospital of St. Petersburg using digital stationary and mobile X-ray machines. In most of the radiographs, it was possible to improve the images contrast, to highlight the objects boundaries. Besides, the method was applied in microfocus X-ray tomography to improve the information content of projection data and improve the reconstruction of the 3D image of the research object. In both the first and second cases, the method showed satisfactory results. The developed method makes it possible to significantly increase the information content of microfocus X-ray images. The obtained practical results make it possible to count on broad prospects for the method application, especially in microfocus X-ray.

Self-Supervised Denoising Image Filter Based on Recursive Deep Neural Network Structure

The purpose of this paper is to propose a novel noise removal method based on deep neural networks that can remove various types of noise without paired noisy and clean data. Because this type of filter generally has relatively poor performance, the proposed noise-to-blur-estimated clean (N2BeC) model introduces a stage-dependent loss function and a recursive learning stage for improved denoised image quality. The proposed loss function regularizes the existing loss function so that the proposed model can better learn image details. Moreover, the recursive learning stage provides the proposed model with an additional opportunity to learn image details. The overall deep neural network consists of three learning stages and three corresponding loss functions. We determine the essential hyperparameters via several simulations. Consequently, the proposed model showed more than 1 dB superior performance compared with the existing noise-to-blur model.

Regional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing

Under foggy and other severe weather conditions, image acquisition equipment is not effective. It often produces an image with low contrast and low scene brightness, which is difficult to use in other image-based applications. The dark channel prior dehazing algorithm will cause the brightness of the image to decrease and sometimes introduce halos in the sky area. To solve this problem, we proposed a region similarity optimisation algorithm based on a dark channel prior. First, a vector comprising RGB layer dark channel value was obtained as the original atmospheric ambient light, and then, the proposed regional similarity linear function was used to adjust the atmospheric ambient light matrix. Next, the transmittance of different colour channels was derived and the multichannel soft matting algorithm was employed to produce more effective transmittance. Finally, the atmospheric ambient light and transmittance were substituted into the atmospheric scattering model to calculate clean images. Experimental results show that the proposed algorithm outperformed the existing mainstream dehazing algorithms in terms of both visual judgement and quality analysis with nonhomogeneous haze datasets. The algorithm not only improves the image details but also improves the brightness and saturation of the dehazing result; therefore, the proposed algorithm is effective in the restoration of the hazy image.

An Image Preprocessing Model of Coal and Gangue in High Dust and Low Light Conditions Based on the Joint Enhancement Algorithm

The lighting facilities are affected due to conditions of coal mine in high dust pollution, which bring problems of dim, shadow, or reflection to coal and gangue images, and make it difficult to identify coal and gangue from background. To solve these problems, a preprocessing model for low-quality images of coal and gangue is proposed based on a joint enhancement algorithm in this paper. Firstly, the characteristics of coal and gangue images are analyzed in detail, and the improvement ways are put forward. Secondly, the image preprocessing flow of coal and gangue is established based on local features. Finally, a joint image enhancement algorithm is proposed based on bilateral filtering. In experimental, K-means clustering segmentation is used to compare the segmentation results of different preprocessing methods with information entropy and structural similarity. Through the simulation experiments for six scenes, the results show that the proposed preprocessing model can effectively reduce noise, improve overall brightness and contrast, and enhance image details. At the same time, it has a better segmentation effect. All of these can provide a better basis for target recognition.

ERDBNet: Enhanced Residual Dense Block Net --A New Net to Rich ESRGAN Image Details

Super resolution is applied in many digital image fields. In many cases, only a set of low-resolution images can be obtained, but the image needs a higher resolution, and then SR needs to be applied. SR technology has undergone years of development. Among them, SRGAN is the key work to introduce GAN into the SR field, which can truly restore a large number of details on the basis of low-pixel pictures. ESRGAN is a further improvement on SRGAN. By removing the BN layer in SRGAN, the effect of artifacts in SRGAN is eliminated. However, there is still a problem that the restoration of information on small and medium scales is not accurate enough. The proposed ERDBNet improve the model on the basis of ESRGAN, and use the ERDB block to replace the original RRDB block. The new structure uses a three-layer dense block to replace the original dense block, and a residual structure of the starting point is added to each dense block. The pre-trained network can reach a PSNR of 30.425 after 200k iterations, and the minimum floating PSNR is only 30.213. Compared with the original structure, it is more stable and performs better in the detail recovery of many low-pixel images.