Regolith textures on Mercury and the Moon

Author(s):  
Anastasia Zharkova ◽  
Mikhail Kreslavsky ◽  
Maria Kolenkina

<p>The surfaces of Mercury and the Moon are covered with a layer of fragmental, highly heterogeneous material known as regolith. Regolith-related processes form short-scale textures seen in the high-resolution images. We carried out a survey of such textures on Mercury and compared them to better-known lunar analogs.</p><p>We surveyed the images obtained by MDIS NAC camera onboard the MESSENGER orbiter toward the end of the mission. We select images of the highest resolution and the finest sampling (less than 2.5 m/pix). We selected and screened ~3000 best images of that data set. To compare the typical surface morphology on Mercury to the Moon we used LROC NAC images. To facilitate the comparison we selected a representative set of LROC images that have the same sampling and sunlight incidence angles as the surveyed MDIS images, and degraded their quality.</p><p>Primarily, lunar and hermian surfaces as seen at high resolution are similar. The majority of decameter-scale topographic features are smooth and subdued due to the presence of regolith layer and its gardening. The majority of small impact craters are shallow and subdued. On the Moon, regolith-covered slopes, both steep and gentle, often have a specific subtle decameter-scale pattern referred as “elephant hide” or “leathery texture”. Its origin is unknown; however, it is almost certainly related to regolith transport. On Mercury, such a pattern is typically not observed: we identified it in a few occasions only.</p><p>Sharp slope breaks, “crisp” morphology and the absence of superposed degraded craters indicate geologically young “fresh” features that are characterized by thin or recently disturbed regolith. We observed fresh morphologies in one large young crater on Mercury; they were similar to their lunar counterparts. Hollows are unique “fresh” hermian features that have no close lunar analogs. They show exceptional sharpness at the highest resolution images, which indicates that their formation is ongoing or extremely recent. We found two more types of fresh morphologies that do not have close lunar analogs. (1) Finely-Textured Slope Patches (FTSP) are patches of finely (meter-scale) textured slopes with sharp outlines. This texture is characterized by a wavy chaotic pattern and occurs amid typical intercrater plains and old impact basins; there are no large young craters or hollows nearby, nor resolvable albedo or color peculiarities close to FTSP locations. They show semblance to some kinds of terrestrial landslides, which might suggest a variant of slide of thick regolith as their formation mechanism. (2) Chevron texture resembles scouring by wind or water in terrestrial environment; however, this cannot suggest a similar formation mechanism hermian conditions. Chevron texture found in one small part of the region with the super resolution images; it is oriented in the same direction. We initially a suggested that it could be related to a ray of a large young crater, but this was not perfectly consistent with observations.</p><p>In addition to the expected morphological similarity of regolith textures on the Moon and Mercury, the hermian surface displaces localized traces of geologically recent processes in the regolith having no lunar analogs.</p>

2020 ◽  
Vol 10 (12) ◽  
pp. 4282
Author(s):  
Ghada Zamzmi ◽  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani

Medical images are acquired at different resolutions based on clinical goals or available technology. In general, however, high-resolution images with fine structural details are preferred for visual task analysis. Recognizing this significance, several deep learning networks have been proposed to enhance medical images for reliable automated interpretation. These deep networks are often computationally complex and require a massive number of parameters, which restrict them to highly capable computing platforms with large memory banks. In this paper, we propose an efficient deep learning approach, called Hydra, which simultaneously reduces computational complexity and improves performance. The Hydra consists of a trunk and several computing heads. The trunk is a super-resolution model that learns the mapping from low-resolution to high-resolution images. It has a simple architecture that is trained using multiple scales at once to minimize a proposed learning-loss function. We also propose to append multiple task-specific heads to the trained Hydra trunk for simultaneous learning of multiple visual tasks in medical images. The Hydra is evaluated on publicly available chest X-ray image collections to perform image enhancement, lung segmentation, and abnormality classification. Our experimental results support our claims and demonstrate that the proposed approach can improve the performance of super-resolution and visual task analysis in medical images at a remarkably reduced computational cost.


2019 ◽  
Vol 11 (21) ◽  
pp. 2593
Author(s):  
Li ◽  
Zhang ◽  
Jiao ◽  
Liu ◽  
Yang ◽  
...  

In the convolutional sparse coding-based image super-resolution problem, the coefficients of low- and high-resolution images in the same position are assumed to be equivalent, which enforces an identical structure of low- and high-resolution images. However, in fact the structure of high-resolution images is much more complicated than that of low-resolution images. In order to reduce the coupling between low- and high-resolution representations, a semi-coupled convolutional sparse learning method (SCCSL) is proposed for image super-resolution. The proposed method uses nonlinear convolution operations as the mapping function between low- and high-resolution features, and conventional linear mapping can be seen as a special case of the proposed method. Secondly, the neighborhoods within the filter size are used to calculate the current pixel, improving the flexibility of our proposed model. In addition, the filter size is adjustable. In order to illustrate the effectiveness of SCCSL method, we compare it with four state-of-the-art methods of 15 commonly used images. Experimental results show that this work provides a more flexible and efficient approach for image super-resolution problem.


Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4601
Author(s):  
Juan Wen ◽  
Yangjing Shi ◽  
Xiaoshi Zhou ◽  
Yiming Xue

Currently, various agricultural image classification tasks are carried out on high-resolution images. However, in some cases, we cannot get enough high-resolution images for classification, which significantly affects classification performance. In this paper, we design a crop disease classification network based on Enhanced Super-Resolution Generative adversarial networks (ESRGAN) when only an insufficient number of low-resolution target images are available. First, ESRGAN is used to recover super-resolution crop images from low-resolution images. Transfer learning is applied in model training to compensate for the lack of training samples. Then, we test the performance of the generated super-resolution images in crop disease classification task. Extensive experiments show that using the fine-tuned ESRGAN model can recover realistic crop information and improve the accuracy of crop disease classification, compared with the other four image super-resolution methods.


2020 ◽  
Vol 8 (4) ◽  
pp. 304-310
Author(s):  
Windra Swastika ◽  
Ekky Rino Fajar Sakti ◽  
Mochamad Subianto

Low-resolution images can be reconstructed into high-resolution images using the Super-resolution Convolution Neural Network (SRCNN) algorithm. This study aims to improve the vehicle license plate number's recognition accuracy by generating a high-resolution vehicle image using the SRCNN. The recognition is carried out by two types of character recognition methods: Tesseract OCR and SPNet. The training data for SRCNN uses the DIV2K dataset consisting of 900 images, while the training data for character recognition uses the Chars74 dataset. The high-resolution images constructed using SRCNN can increase the average accuracy of vehicle license plate number recognition by 16.9 % using Tesseract and 13.8 % with SPNet.


Author(s):  
Alejandro Güemes ◽  
Carlos Sanmiguel Vila ◽  
Stefano Discetti

A data-driven approach to reconstruct high-resolution flow fields is presented. The method is based on exploiting the recent advances of SRGANs (Super-Resolution Generative Adversarial Networks) to enhance the resolution of Particle Image Velocimetry (PIV). The proposed approach exploits the availability of incomplete projections on high-resolution fields using the same set of images processed by standard PIV. Such incomplete projection is made available by sparse particle-based measurements such as super-resolution particle tracking velocimetry. Consequently, in contrast to other works, the method does not need a dual set of low/high-resolution images, and can be applied directly on a single set of raw images for training and estimation. This data-enhanced particle approach is assessed employing two datasets generated from direct numerical simulations: a fluidic pinball and a turbulent channel flow. The results prove that this data-driven method is able to enhance the resolution of PIV measurements even in complex flows without the need of a separate high-resolution experiment for training.


Author(s):  
Zheng Wang ◽  
Mang Ye ◽  
Fan Yang ◽  
Xiang Bai ◽  
Shin'ichi Satoh

Person re-identification (REID) is an important task in video surveillance and forensics applications. Most of previous approaches are based on a key assumption that all person images have uniform and sufficiently high resolutions. Actually, various low-resolutions and scale mismatching always exist in open world REID. We name this kind of problem as Scale-Adaptive Low Resolution Person Re-identification (SALR-REID). The most intuitive way to address this problem is to increase various low-resolutions (not only low, but also with different scales) to a uniform high-resolution. SR-GAN is one of the most competitive image super-resolution deep networks, designed with a fixed upscaling factor. However, it is still not suitable for SALR-REID task, which requires a network not only synthesizing high-resolution images with different upscaling factors, but also extracting discriminative image feature for judging person’s identity. (1) To promote the ability of scale-adaptive upscaling, we cascade multiple SRGANs in series. (2) To supplement the ability of image feature representation, we plug-in a reidentification network. With a unified formulation, a Cascaded Super-Resolution GAN (CSR-GAN) framework is proposed. Extensive evaluations on two simulated datasets and one public dataset demonstrate the advantages of our method over related state-of-the-art methods.


2000 ◽  
Vol 643 ◽  
Author(s):  
Hiroyuki Takakura ◽  
Akiji Yamamoto ◽  
An Pang Tsai

AbstractThe models of decagonal Al72Ni20Co8 quasicrystal with the space group of P105/mmc were refined on the basis of single crystal x-ray diffraction data set using the 5D description. The results of a structure model derived from Al13Fe4-type approximant crystal and Burkov model are compared. The former gives ω R=0.045 and R=0.063 for 449 reflections with 103 parameters and a resonable chemical composition of Al71.2TM28.8 (TM=transition metals). The projected structure in consistent with high resolution images of this material. On the other hand, the latter gives ωR=0.161 and R=0.193 for 55 parameters and a compositon of Al64.6TM35.1.


Author(s):  
CHIEN-YU CHEN ◽  
YU-CHUAN KUO ◽  
CHIOU-SHANN FUH

In this paper we propose a technique that reconstructs high-resolution images with improved super-resolution algorithms, based on Irani and Peleg iterative method, and employs our suggested initial interpolation, robust image registration, automatic image selection and image enhancement post-processing. When the target of reconstruction is a moving object with respect to a stationary camera, high-resolution images can still be reconstructed, whereas previous systems only work well when we move the camera and the displacement of the whole scene is the same.


2011 ◽  
Vol 204-210 ◽  
pp. 1336-1341
Author(s):  
Zhi Gang Xu ◽  
Xiu Qin Su

Super-resolution (SR) restoration produces one or a set of high resolution images from low-resolution observations. In particular, SR restoration involves many multidisciplinary studies. A review on recent SR restoration approaches was given in this paper. First, we introduced the characteristics and framework of SR restoration. The state of the art in SR restoration was surveyed by taxonomy. Then we summarized and analyzed the existing algorithms of registration and reconstruction. A comparison of performing differences between these methods would only be valid given. After that we discussed the SR problems of color images and compressed videos. At last, we concluded with some thoughts about future directions.


2015 ◽  
Vol 713-715 ◽  
pp. 1574-1578
Author(s):  
Yan Zhang ◽  
Pan Pan Jiang

Aiming at the characteristics of the UAV camera, camera data nowadays, a new improved method is proposed based on putting the low-resolution video reconstruction into high-resolution video. First, the low-resolution video frame is done spectrum analysis by Fourier transform. Second, find the maximum gradient descent point to determine the cut off frequency. Finally making use of high-resolution images with high frequency detail, then motion compensated. Through POCS algorithm, then iterated, obtaining super-resolution reconstruction video and realizing the above by MATLAB simulation.


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