Efficient Iterative Regularization Method for Total Variation-Based Image Restoration

Total variation (TV) regularization has received much attention in image restoration applications because of its advantages in denoising and preserving details. A common approach to address TV-based image restoration is to design a specific algorithm for solving typical cost function, which consists of conventional ℓ2 fidelity term and TV regularization. In this work, a novel objective function and an efficient algorithm are proposed. Firstly, a pseudoinverse transform-based fidelity term is imposed on TV regularization, and a closely-related optimization problem is established. Then, the split Bregman framework is used to decouple the complex inverse problem into subproblems to reduce computational complexity. Finally, numerical experiments show that the proposed method can obtain satisfactory restoration results with fewer iterations. Combined with the restoration effect and efficiency, this method is superior to the competitive algorithm. Significantly, the proposed method has the advantage of a simple solving structure, which can be easily extended to other image processing applications.

Research and Application of Ancient Chinese Pattern Restoration Based on Deep Convolutional Neural Network

In recent years, deep learning, as a very popular artificial intelligence method, can be said to be a small area in the field of image recognition. It is a type of machine learning, actually derived from artificial neural networks, and is a method used to learn the characteristics of sample data. It is a multilayer network, which can learn the information from the bottom to the top of the image through the multilayer network, so as to extract the characteristics of the sample, and then perform identification and classification. The purpose of deep learning is to make the machine have the same analytical and learning capabilities as the human brain. The ability of deep learning in data processing (including images) is unmatched by other methods, and its achievements in recent years have left other methods behind. This article comprehensively reviews the application research progress of deep convolutional neural networks in ancient Chinese pattern restoration and mainly focuses on the research based on deep convolutional neural networks. The main tasks are as follows: (1) a detailed and comprehensive introduction to the basic knowledge of deep convolutional neural and a summary of related algorithms along the three directions of text preprocessing, learning, and neural networks are provided. This article focuses on the related mechanism of traditional pattern repair based on deep convolutional neural network and analyzes the key structure and principle. (2) Research on image restoration models based on deep convolutional networks and adversarial neural networks is carried out. The model is mainly composed of four parts, namely, information masking, feature extraction, generating network, and discriminant network. The main functions of each part are independent and interdependent. (3) The method based on the deep convolutional neural network and the other two methods are tested on the same part of the Qinghai traditional embroidery image data set. From the final evaluation index of the experiment, the method in this paper has better evaluation index than the traditional image restoration method based on samples and the image restoration method based on deep learning. In addition, from the actual image restoration effect, the method in this paper has a better image restoration effect than the other two methods, and the restoration results produced are more in line with the habit of human observation with the naked eye.

Use of Nanocomposite Resin Materials for Dental Restoration in Elderly Patients

Tooth defects can affect not only periodontal tissue but also the whole body if not restored in good time. Composite resin materials are commonly used filling materials in dental restorations, but they have low material strength and are likely to cause insufficient secondary caries. Improving the ability of composite resin materials to restore defective teeth has thus become the focus of research interest. Nanocomposite materials are widely used in dentistry because of their good design characteristics, wide indications, strong restorative power, and high economic efficiency. However, whether they cause respiratory tract inflammation or tissue damage due to their large specific surface area still needs further investigation. This study compared the effects of nanocomposite resin materials with those of traditional light-curable composite resin materials on the restoration of dental defects in elderly patients and found that nanomaterials could not only reduce the incidence of tooth sensitivity and tooth pain after restoration but also improve the aesthetic outcomes of the tooth. In addition to the restoration effect, the occurrence of adverse reactions in patients who underwent dental restoration using nanomaterials within 2 years after the procedure was significantly lower than that in patients who underwent dental restoration using traditional materials. These results indicate that the nanocomposite resin material improved the restoration effect in elderly patients without increasing their risk for adverse reactions. Therefore, nanocomposite resin materials should be used as the preferred filling material for dental restoration in elderly patients with dental defects.

Hepatotoxicity of methoxychlor and camel milk restoration

Purpose The purpose of this paper is to investigate the restoration effect of camel's milk against methoxychlor induced liver toxicity. Design/methodology/approach The present study was carried out to investigate the restoration effect of camel's milk against methoxychlor induced liver toxicity. Findings Methoxychlor (MXC) caused a significant increase in serum transaminases (aspartate transaminase and alanine transaminase) and alkaline phosphatase, while MXC induced a significant reduction in total protein and albumin levels. MXC significantly inhibited lipid peroxidation and markedly enhanced glutathione in liver homogenate. Pathological damages as degeneration and coagulative necrosis of hepatocytes were established in liver. Newly formed bile ducteules denotes neoplastic changes in the portal tract with abnormal mitotic pattern were associated with the long-term exposure. Originality/value The present study concluded that camel milk treatment may play a protective role against methoxychlor-induced liver damage in rats.

Research on dark channel dehazing of single-image based on non-dispersive infrared (NDIR) detection technology

This paper presents an experimental study on the non-dispersive infrared (NDIR) detection technology and dark channel dehazing technology. Based on the analysis of Beer-Lambert Law and differential carbon dioxide detection principle, this paper proposes an atmospheric light value estimation algorithm based on NDIR detection technology. First, the change characteristics of the gas concentration in indoor smoky environment are collected and analyzed. Then appropriate weighting coefficients are chosen based on the gas characteristics to estimate the atmospheric light value. Finally, the digital image dehazing technology through dark channel prior is used for calculation to obtain a haze-free image with high quality and high resolution. The experiment in this paper proves the feasibility of combining NDIR detection technology with dehazing technology, and its ability to improve image quality and achieve better restoration effect.

Application of Adaptive Image Restoration Algorithm Based on Sparsity of Block Structure in Environmental Art Design

Image restoration is a research hotspot in computer vision and computer graphics. It uses the effective information in the image to fill in the information of the designated damaged area. This has high application value in environmental design, film and television special effects production, old photo restoration, and removal of text or obstacles in images. In traditional sparse representation image restoration algorithms, the size of dictionary atoms is often fixed. When repairing the texture area, the dictionary atom will be too large to cause blurring. When repairing a smooth area, the dictionary atom is too small to cause the extension of the area, which affects the image repair effect. In this paper, the structural sparsity of the block to be repaired is used to adjust the repair priority. By analyzing the structure information of the repair block located in different regions such as texture, edge, and smoothing, the size of the dictionary atom is adaptively determined. This paper proposes a color image restoration method that adaptively determines the size of dictionary atoms and discusses a model based on the partial differential equation restoration method. Through simulation experiments combined with subjective and objective standards, the repair results are evaluated and analyzed. The simulation results show that the algorithm can effectively overcome the shortcomings of blurred details and region extension in fixed dictionary restoration, and the restoration effect has been significantly improved. Compared with the results of several other classic algorithms, it shows the effectiveness of the algorithm in this paper.

Monitoring and Evaluating Restoration Vegetation Status in Mine Region Using Remote Sensing Data: Case Study in Inner Mongolia, China

The ecological restoration of mining areas is very important, and repeated field surveys are inefficient in large-scale vegetation monitoring. The coal mining industry is currently facing the challenge of the lack of appropriate methods for monitoring restoration processes. This study used an open pit coal mine in Dongsheng District, Inner Mongolia, China as an example, and used the 2011–2018 Landsat TM/ETM+ and OLI images to monitor and evaluate vegetation restoration activity of the coal mine. The average value of the monthly maximum value of vegetation index in the growing season was selected as the basic indicator for studying vegetation and bare soil changes. The growth root normalized differential vegetation index (GRNDVI) and GRNDVI anomaly method indicated that the constructed land type change factor was used to study the growth of mine vegetation and change of the range of bare land in the entire mining region. We found that westward mining activities started from 2012, and vegetation was restored in the eastern original mining region from 2013. The restoration vegetation areas from 2015 to 2016 and from 2017 to 2018 were larger than those in the other restoration years. Moreover, areas of expanded bare land from 2011 to 2012, and from 2017 to 2018 were larger than those in the other expansion years. The restoration vegetation growth changes were compared with those of the natural vegetation growth. Results showed that the restoration vegetation growth trend was considerably similar with that of the natural vegetation. Inter-annual restoration effects were analyzed by constructing the effect of the area-average factor and using vegetation growth data. Accordingly, the restoration vegetation effects were best in 2014 and 2016. Comprehensive restoration effect was analyzed using the weighted evaluation method to obtain the overall restoration effects of the coal mine. Results showed that the comprehensive restoration effect is inclined to the inferior growth state. This study conducted a preliminary evaluation of mine restoration vegetation, thereby providing a promising way for the future monitoring and evaluation of such processes.

UAV monitoring of urban stream restoration sustainability

<p>High-resolution imaging using unmanned aerial vehicles (UAV, UAS, drones) opened up in the last decade a new potential for a detailed, reliable, operable, and affordable approach for riverscape monitoring. Based on the experience with the pilot research on UAV detection of fluvial dynamics on natural and modified streams, we have proposed a framework for the assessment of the sustainability of stream restoration projects based on UAV imaging and photogrammetry. The approach focuses on aspects where the high-resolution UAV imagery can bring reliable and quantitative information, applicable for assessing restoration success and incorporation into standard assessment schemes.</p><p>We distinguish four critical aspects of stream restorations, where the UAV monitoring can provide reliable quantitative information, applicable for assessment of stream restoration success or failure: (i) Restoration effect, (ii) Dynamics of fluvial processes, (iii) Hydrological connectivity, and (iv) Riparian vegetation. For each aspect, there are derived relevant indicators, allowing quantitative assessment and scoring.</p><p>We have tested the framework on the evaluation of restorations on three urban streams in the metropolitan area of Prague, Czech Republic, which were subject of revitalization in the past decade. We have maintained regular recurrent UAV monitoring campaigns of these streams over four years, which enabled tracking the restorations since their completion and identifying the positive aspects and the failures in the sustainability of the realized restoration projects. </p><p>UAV monitoring enabled to identify stream restoration features that would be hard or impossible to assess by other mapping techniques. As for the restoration effect, the UAV assessment revealed that although the basic goals of restoration projects were fulfilled, the newly shaped stream patterns significantly differ from the approved restoration plans. The restored channels are typically less complex and featuring simpler geometry than planned. Multitemporal assessment enabled to track stream instability and to measure the extent of bank erosion. UAV monitoring over a low flow period enabled to identify the stream segments where the inappropriate channel transformation led to disruptions in hydrological connectivity, and to detect and measure the extent of eutrophication in the stream and the newly created shallow ponds. UAV monitoring also enabled tracking the progress of vegetation succession after the restoration and quantitatively assessing the extent of riparian shading as a substantial element of sustainability of stream restoration.</p><p>Despite the limitations stemming from the nature of optical sensing, UAV monitoring proved to be a highly efficient and reliable technique suitable for evaluating stream restoration projects with versatile applications even in the urban environment’s specific conditions.</p>