GMSRI: A Texture-Based Martian Surface Rock Image Dataset

CNN-based Martian rock image processing has attracted much attention in Mars missions lately, since it can help planetary rover autonomously recognize and collect high value science targets. However, due to the difficulty of Martian rock image acquisition, the accuracy of the processing model is affected. In this paper, we introduce a new dataset called “GMSRI” that is a mixture of real Mars images and synthetic counterparts which are generated by GAN. GMSRI aims to provide a set of Martian rock images sorted by the texture and spatial structure of rocks. This paper offers a detailed analysis of GMSRI in its current state: Five sub-trees with 28 leaf nodes and 30,000 images in total. We show that GMSRI is much larger in scale and diversity than the current same kinds of datasets. Constructing such a database is a challenging task, and we describe the data collection, selection and generation processes carefully in this paper. Moreover, we evaluate the effectiveness of the GMSRI by an image super-resolution task. We hope that the scale, diversity and hierarchical structure of GMSRI can offer opportunities to researchers in the Mars exploration community and beyond.

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Research on Image Denoising and Super-Resolution Reconstruction Technology of Multiscale-Fusion Images

Mobile Information Systems ◽

10.1155/2021/5184688 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Size Li ◽

Pengjiang Qian ◽

Xin Zhang ◽

Aiguo Chen

Keyword(s):

Image Processing ◽

Receptive Field ◽

Image Denoising ◽

State Of The Art ◽

Experimental Studies ◽

Super Resolution ◽

Image Resolution ◽

Reconstruction Method ◽

Model Learning ◽

Image Super Resolution

Image denoising and image super-resolution reconstruction are two important techniques for image processing. Deep learning is used to solve the problem of image denoising and super-resolution reconstruction in recent years, and it usually has better results than traditional methods. However, image denoising and super-resolution reconstruction are studied separately by state-of-the-art work. To optimally improve the image resolution, it is necessary to investigate how to integrate these two techniques. In this paper, based on Generative Adversarial Network (GAN), we propose a novel image denoising and super-resolution reconstruction method, i.e., multiscale-fusion GAN (MFGAN), to restore the images interfered by noises. Our contributions reflect in the following three aspects: (1) the combination of image denoising and image super-resolution reconstruction simplifies the process of upsampling and downsampling images during the model learning, avoiding repeated input and output images operations, and improves the efficiency of image processing. (2) Motivated by the Inception structure and introducing a multiscale-fusion strategy, our method is capable of using the multiple convolution kernels with different sizes to expand the receptive field in parallel. (3) The ablation experiments verify the effectiveness of each employed loss measurement in our devised loss function. And our experimental studies demonstrate that the proposed model can effectively expand the receptive field and thus reconstruct images with high resolution and accuracy and that the proposed MFGAN method performs better than a few state-of-the-art methods.

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A Review on Medical Image Super Resolution with Application of Deep Learning

SMART MOVES JOURNAL IJOSCIENCE ◽

10.24113/ijoscience.v7i2.368 ◽

2021 ◽

Vol 7 (2) ◽

pp. 25-29

Author(s):

Kajol Singh ◽

Manish Saxena

Keyword(s):

Deep Learning ◽

Medical Imaging ◽

Medical Image ◽

Super Resolution ◽

Image Resolution ◽

Loss Functions ◽

Acquisition Time ◽

Current State ◽

Image Acquisition Time ◽

Image Super Resolution

Super resolution problems are often discussed in medical imaging. The spatial resolution of medical images is insufficient due to limitations such as image acquisition time, low radiation dose or hardware limitations. Various super-resolution methods have been proposed to solve these problems, such as optimization or learning-based approaches. Recently, deep learning methodologies have become a thriving technology and are evolving at an exponential rate. We believe we need to write a review to illustrate the current state of deep learning in super-resolution medical imaging. In this article, we provide an overview of image resolution and the deep learning introduced in super resolution. This document describes super resolution for single images versus super resolution for multiple images, evaluation metrics and loss functions.

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Single Image Super-Resolution Restoration of TGO CaSSIS Colour Images: Demonstration with Perseverance Rover Landing Site and Mars Science Targets

Remote Sensing ◽

10.3390/rs13091777 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1777

Author(s):

Yu Tao ◽

Susan J. Conway ◽

Jan-Peter Muller ◽

Alfiah R. D. Putri ◽

Nicolas Thomas ◽

...

Keyword(s):

Spatial Resolution ◽

Imaging System ◽

Scientific Information ◽

Super Resolution ◽

Landing Site ◽

Single Image ◽

Martian Surface ◽

Generative Adversarial Network ◽

Image Super Resolution ◽

Single Image Super Resolution

The ExoMars Trace Gas Orbiter (TGO)’s Colour and Stereo Surface Imaging System (CaSSIS) provides multi-spectral optical imagery at 4-5m/pixel spatial resolution. Improving the spatial resolution of CaSSIS images would allow greater amounts of scientific information to be extracted. In this work, we propose a novel Multi-scale Adaptive weighted Residual Super-resolution Generative Adversarial Network (MARSGAN) for single-image super-resolution restoration of TGO CaSSIS images, and demonstrate how this provides an effective resolution enhancement factor of about 3 times. We demonstrate with qualitative and quantitative assessments of CaSSIS SRR results over the Mars2020 Perseverance rover’s landing site. We also show examples of similar SRR performance over 8 science test sites mainly selected for being covered by HiRISE at higher resolution for comparison, which include many features unique to the Martian surface. Application of MARSGAN will allow high resolution colour imagery from CaSSIS to be obtained over extensive areas of Mars beyond what has been possible to obtain to date from HiRISE.

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Single-image super-resolution improvement of X-ray single-particle diffraction images using convolutional neural network

10.26434/chemrxiv-2021-vtvjn ◽

2021 ◽

Author(s):

Atsushi Tokuhisa ◽

Yoshinobu Akinaga ◽

Kei Terayama ◽

Yasushi Okuno

Keyword(s):

Neural Network ◽

Image Processing ◽

Structural Analysis ◽

Single Particle ◽

Super Resolution ◽

Diffraction Image ◽

Single Image ◽

X Ray ◽

Image Super Resolution ◽

Single Image Super Resolution

Femtosecond X-ray pulse lasers are promising probes for elucidating the multi-conformational states of biomolecules because they enable snapshots of single biomolecules to be observed as coherent diffraction images. Multi-image processing using an X-ray free electron laser has proven to be a successful structural analysis method for viruses. However, some difficulties remain in single-particle analysis (SPA) for flexible biomolecules with sizes of 100 nm or less. Owing to the multi-conformational states of biomolecules and the noisy character of diffraction images, diffraction image improvement by multi-image processing is not always effective for such molecules. Here, a single-image super-resolution (SR) model was constructed using a SR convolutional neural network (SRCNN). Data preparation was performed in silico to consider the actual observation situation with unknown molecular orientations, and fluctuation of molecular structure and incident X-ray intensity. It was demonstrated that the trained SRCNN model improved the single-particle diffraction image quality, which corresponded to an observed image with an incident X-ray intensity; i.e., approximately three to seven times higher than the original X-ray intensity, while retaining the individuality of the diffraction images. The feasibility of SPA for flexible biomolecules with sizes of 100 nm or less was dramatically increased by introducing the SRCNN improvement at the beginning of the variety structural analysis schemes.

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Automated electron tomography: from data collection to image processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100136507 ◽

1995 ◽

Vol 53 ◽

pp. 26-27

Author(s):

Weiping Liu ◽

Jennifer Fung ◽

W.J. de Ruijter ◽

Hans Chen ◽

John W. Sedat ◽

...

Keyword(s):

Image Processing ◽

Data Collection ◽

Structural Information ◽

Imaging Technique ◽

Electron Tomography ◽

Ccd Camera ◽

Automated Data Collection ◽

Full Control ◽

Transmission Electron ◽

Amorphous Structures

Electron tomography is a technique where many projections of an object are collected from the transmission electron microscope (TEM), and are then used to reconstruct the object in its entirety, allowing internal structure to be viewed. As vital as is the 3-D structural information and with no other 3-D imaging technique to compete in its resolution range, electron tomography of amorphous structures has been exercised only sporadically over the last ten years. Its general lack of popularity can be attributed to the tediousness of the entire process starting from the data collection, image processing for reconstruction, and extending to the 3-D image analysis. We have been investing effort to automate all aspects of electron tomography. Our systems of data collection and tomographic image processing will be briefly described.To date, we have developed a second generation automated data collection system based on an SGI workstation (Fig. 1) (The previous version used a micro VAX). The computer takes full control of the microscope operations with its graphical menu driven environment. This is made possible by the direct digital recording of images using the CCD camera.

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