Denoising of Tourist Street Scene Image Based on ROF Model of Second-Order Partial Differential Equation

The noise pollution in tourist street view images is caused by various reasons. A major challenge that researchers have been facing is to find a way to effectively remove noise. Although in the past few decades people have proposed many methods of denoising tourist street scene images, the research on denoising technology of tourist street scene images is still not outdated. There is no doubt that it has become a basic and important research topic in the field of digital image processing. The evolutionary diffusion method based on partial differential equations is helpful to improve the quality of noisy tourist street scene images. This method can process tourist street scene images according to people’s expected diffusion behavior. The adaptive total variation model proposed in this paper is improved on the basis of the total variation model and the Gaussian thermal diffusion model. We analyze the classic variational PDE-based denoising model and get a unified variational PDE energy functional model. We also give a detailed analysis of the diffusion performance of the total variational model and then propose an adaptive total variational diffusion model. By improving the diffusion coefficient and introducing a curvature operator that can distinguish details such as edges, it can effectively denoise the tourist street scene image, and it also has a good effect on avoiding the step effect. Through the improvement of the ROF model, the loyalty term and regular term of the model are parameterized, the adaptive total variation denoising model of this paper is established, and a detailed analysis is carried out. The experimental results show that compared with some traditional denoising models, the model in this paper can effectively suppress the step effect in the denoising process, while protecting the texture details of the edge area of the tourist street scene image. In addition, the model in this paper is superior to traditional denoising models in terms of denoising performance and texture structure protection.

Image Compression Approach using Segmentation and Total Variation Regularization

In this paper we present a hybrid model for image compression based on segmentation and total variation regularization. The main motivation behind our approach is to offer decode image with immediate access to objects/features of interest. We are targeting high quality decoded image in order to be useful on smart devices, for analysis purpose, as well as for multimedia content-based description standards. The image is approximated as a set of uniform regions: The technique will assign well-defined members to homogenous regions in order to achieve image segmentation. The Adaptive fuzzy c-means (AFcM) is a guide to cluster image data. A second stage coding is applied using entropy coding to remove the whole image entropy redundancy. In the decompression phase, the reverse process is applied in which the decoded image suffers from missing details due to the coarse segmentation. For this reason, we suggest the application of total variation (TV) regularization, such as the Rudin-Osher-Fatemi (ROF) model, to enhance the quality of the coded image. Our experimental results had shown that ROF may increase the PSNR and hence offer better quality for a set of benchmark grayscale images.

A Novel Approach for Predicting the Height of the Water-Flow Fracture Zone in Undersea Safety Mining

The height of the water-flow fracture zone (WFZ) is an important reference for designing the size of a waterproof crown pillar. Once the WFZ is connected with the sea, there will be catastrophic consequences, especially for undersea mining. This study suggests using a rotating forest (RoF) model to predict the height of the WFZ for the evaluation of the size of a waterproof crown pillar. To train and test the RoF model, five indicators with major influencing factors on undersea safety mining were determined, 107 field-measured mining datasets were collected, 75 (70%) datasets were used for training, and 32 (30%) datasets were used for model testing. At the same time, the random forest ensemble algorithm (RFR) and support vector machine (SVM) models were introduced for comparison and verification; in the end, the tested results were evaluated by RMSE (root-mean-square error) and R2. The comparison shows that the predicted results from the RoF model are significantly better than those from the RFR and SVM models. An importance analysis of the impact indicators shows that the mining height and depth have significant impacts on the prediction results. The development height of the WFZ in undersea safety mining was predicted via the RoF model. The predicted results via the RoF model were verified by field observations using panoramic borehole televiewers. The RoF prediction results are consistent with the observation results at all depths. Compared with the other two models, the RoF model has the smallest average absolute error at 2.87%. The results show that the RoF model can be applied to predict the height of the WFZ in undersea mining, which could be an effective way of minimizing the mineral resource waste in the study area and in other similar areas in the world under the premise of mine safety.

Erratum: Lou, X.; Jia, Z.; Yang, J.; Kasabov, N. Change Detection in SAR Images Based on the ROF Model Semi-Implicit Denoising Method. Sensors 2019, 19, 1179

The authors wish to make the following erratum to this paper [...]

Change Detection in SAR Images Based on the ROF Model Semi-Implicit Denoising Method

The explicit solution of the traditional ROF model in image denoising has the disadvantages of unstable results and requiring many iterations. To solve the problem, a new method, ROF model semi-implicit denoising, is proposed in this paper and applied to change detections of synthetic aperture radar (SAR) images. All remote sensing images used in this article have been calibrated by ENVI software. First, the ROF model semi-implicit denoising method is used to denoise the remote sensing images. Second, for the denoised images, difference images are obtained by the logarithmic ratio and mean ratio methods. The final difference image is obtained by principal component analysis fusion (PCA fusion) of the two difference images. Finally, the final difference image is clustered by fuzzy local information C-means clustering (FLICM) to obtain the change regions. The research results show that the proposed method has high detection accuracy and time operation efficiency.