Restoration Simulation of Image Quality Degradation of Nanosensors Based on Virtual Reality

The texture complexity of traditional sensor image degradation restoration methods is high and the restoration effect is reduced. For this reason, a virtual reality-based image quality degradation recovery method for nanosensors is designed in this paper. First, the image quality degradation model of nanometer sensor is constructed based on virtual reality technology. Then, the noise characteristics of the degraded image are analyzed. On the premise of retaining the original image information, the diffusion coefficients in the vertical and horizontal directions are calculated to obtain the expression of adaptive filter (ADF) in the image with noise, so as to complete the image denoising process. On the basis of texture complexity analysis, singular value decomposition detection and alpha channel calculation are completed, and image quality degradation recovery of nanosensor is achieved through synthesis operation. The experimental results show that the texture complexity of the recovered images is lower than 0.54, the average absolute error percentage of the recovered images is only 10%, and the P-R value is high, which fully demonstrates the effectiveness of the offered procedure.

Application of Gaofen-5 Hyperspectral Data in Uranium Exploration: A Case Study of Weijing in Inner Mongolia, China

Gaofen-5 (GF-5) satellite is the world's first full-spectrum hyperspectral satellite to achieve comprehensive observations of the atmosphere and land. The Advanced Hyperspectral Imager (AHSI) carried by GF-5 can acquire 330-chanel imagery covering 390 - 2500 nm. However, the application of GF-5 AHSI imagery in uranium exploration is currently unknown. In this paper, the AHSI imagery was used for prospecting uranium mineralization in the Weijing, Inner Mongolia, China. The matched filter (MF) and threshold segmentation were used for mapping goethite, Al-high, Al-medium and Al-poor sericite. And the principal component analysis (PCA) and gray-level co-occurrence matrix (GLCM) were used to extract the texture information of the study area. Subsequently, combined with geological information, the relationship between alteration information, texture complexity and uranium mineralization was discussed, and it was pointed out that goethite, Al-medium, Al-poor sericite and texture complexity in this area can be used as indicators of uranium mineralization. Finally, three prospects were delineated, which will guide the follow-up uranium exploration in this area and promote the application of GF-5 AHSI data.

Fast ISP coding mode optimization algorithm based on CU texture complexity for VVC

In lately published video coding standard Versatile Video Coding (VVC/ H.266), the intra sub-partitions (ISP) coding mode is proposed. It is efficient for frames with rich texture, but less efficient for frames that are very flat or constant. In this paper, by comparing and analyzing the rate distortion cost (RD-cost) of coding unit (CU) with different texture features for using and not using ISP(No-ISP) coding mode, it is observed that CUs with simple texture can skip ISP coding mode. Based on this observation, a fast ISP coding mode optimization algorithm based on CU texture complexity is proposed, which aims to determine whether a CU needs to use ISP coding mode in advance by calculating CU texture complexity, so as to reduce the computation complexity of ISP. The experimental results show that under All Intra (AI) configuration, the coding time can be reduced by 7%, while the BD rate only increase by 0.09%.

A Holistic Approach Towards Surface Topography Analyses for Ice Tribology Applications

A surface texture can be subdivided into three categories based on the magnitude of its wavelengths, i.e., macro-geometrical form, waviness, and roughness (from largest to smallest). Together, these components define how a surface will interact with the opposing surface. In most ice tribology studies, <2% of the entire sample surface is topographically analyzed. Although such a small percentage of the entire surface area generally provides statistically relevant information, the missing information about the texture complexity on a larger scale might reduce the possibility of accurately explaining the resulting tribological behavior. The purpose of this study was to review the existing surface measurement methods related to ice tribology and to present a holistic approach towards surface topography measurements for ice tribology applications. With the holistic surface measurement approach, the entire sample surfaces are scanned, and the measured data is analyzed on different magnitude levels. The discussed approach was applied to sandblasted steel samples which were afterward tested on two different ice tribometers. The experimental results showed that additional information about the sample surface topography enabled a better understanding of the ice friction mechanisms and allowed for a more straightforward correlation between the sample surface topography and its ice friction response.

Preparatory Experiments Regarding Human Brain Perception and Reasoning of Image Complexity for Synthetic Color Fractal and Natural Texture Images via EEG

Texture plays an important role in computer vision in expressing the characteristics of a surface. Texture complexity evaluation is important for relying not only on the mathematical properties of the digital image, but also on human perception. Human subjective perception verbally expressed is relative in time, since it can be influenced by a variety of internal or external factors, such as: Mood, tiredness, stress, noise surroundings, and so on, while closely capturing the thought processes would be more straightforward to human reasoning and perception. With the long-term goal of designing more reliable measures of perception which relate to the internal human neural processes taking place when an image is perceived, we firstly performed an electroencephalography experiment with eight healthy participants during color textural perception of natural and fractal images followed by reasoning on their complexity degree, against single color reference images. Aiming at more practical applications for easy use, we tested this entire setting with a WiFi 6 channels electroencephalography (EEG) system. The EEG responses are investigated in the temporal, spectral and spatial domains in order to assess human texture complexity perception, in comparison with both textural types. As an objective reference, the properties of the color textural images are expressed by two common image complexity metrics: Color entropy and color fractal dimension. We observed in the temporal domain, higher Event Related Potentials (ERPs) for fractal image perception, followed by the natural and one color images perception. We report good discriminations between perceptions in the parietal area over time and differences in the temporal area regarding the frequency domain, having good classification performance.

Fast ISP Coding Mode Optimization Algorithm Based on CU Texture Complexity for VVC

In lately published video coding standard Versatile Video Coding (VVC/ H.266), the intra sub-partitions (ISP) coding mode is proposed. It is efficient for frames with rich texture, but less efficient for frames that are very flat or constant. In this paper, by comparing and analyzing the rate distortion cost (RD-cost) of coding unit (CU) with different texture features for using and not using ISP(No-ISP) coding mode, it is found that CUs with simple texture can get better coding performance in No-ISP coding mode. Based on this observations, a fast ISP coding mode optimization algorithm based on CU texture complexity is proposed, which aims to determine whether CU needs to use ISP coding mode in advance by calculating CU texture complexity, so as to reduce the computation complexity of ISP. The experimental results show that under All Intra (AI) configuration, the coding time can be reduced by 7%, while the BD-rate only increase by 0.09%.

Semantic Segmentation for SAR Image Based on Texture Complexity Analysis and Key Superpixels

In recent years, regional algorithms have shown great potential in the field of synthetic aperture radar (SAR) image segmentation. However, SAR images have a variety of landforms and a landform with complex texture is difficult to be divided as a whole. Due to speckle noise, traditional over-segmentation algorithm may cause mixed superpixels with different labels. They are usually located adjacent to two areas or contain more noise. In this paper, a new semantic segmentation method of SAR images based on texture complexity analysis and key superpixels is proposed. Texture complexity analysis is performed and on this basis, mixed superpixels are selected as key superpixels. Specifically, the texture complexity of the input image is calculated by a new method. Then a new superpixels generation method called neighbourhood information simple linear iterative clustering (NISLIC) is used to over-segment the image. For images with high texture complexity, the complex areas are first separated and key superpixels are selected according to certain rules. For images with low texture complexity, key superpixels are directly extracted. Finally, the superpixels are pre-segmented by fuzzy clustering based on the extracted features and the key superpixels are processed at the pixel level to obtain the final result. The effectiveness of this method has been successfully verified on several kinds of images. Comparing with the state-of-the-art algorithms, the proposed algorithm can more effectively distinguish different landforms and suppress the influence of noise, so as to achieve semantic segmentation of SAR images.

Blind Detection of Partial-Color-Manipulation Based on Self-PRNU Estimation

This article describes how to detect color manipulation which is a commonly used method in the field of digital image forgery. The difficulty that hue forgery does not change the image edges, shapes and gradations brings certain challenge to authenticity detection. Current methods utilize the PRNU from multiple un-tampered images, requiring the camera type to be known. However, the increasing varieties of digital devices greatly complicates the preparation of prior knowledge. This article proposes a blind detection method for partial color manipulation based on self-PRNU of suspicious image, eliminating the necessity of acquiring camera information. The authors estimate the PRNU of suspicious image by removing the regions due to its texture complexity. The tamper region is detected by calculating the correlation between estimated PRNU and residual noise. As to partial manipulation detection, an introduced threshold of connected components is used to reduce the false positive. The experimental results show that the method can effectively detect and locate the partial color manipulation.