scholarly journals Three-Dimensional Laser Imaging with a Variable Scanning Spot and Scanning Trajectory

Photonics ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 173
Author(s):  
Ao Yang ◽  
Jie Cao ◽  
Yang Cheng ◽  
Chuanxun Chen ◽  
Qun Hao
Keyword(s):  
High Resolution ◽  
Imaging System ◽  
Three Dimensional ◽  
Super Resolution ◽  
Structural Similarity ◽  
Depth Image ◽  
Reconstruction Method ◽  
Fixed Size ◽  
Laser Imaging ◽  
Image Reconstruction Method

Traditional lidar scans the target with a fixed-size scanning spot and scanning trajectory. Therefore, it can only obtain the depth image with the same pixels as the number of scanning points. In order to obtain a high-resolution depth image with a few scanning points, we propose a scanning and depth image reconstruction method with a variable scanning spot and scanning trajectory. Based on the range information and the proportion of the area of each target (PAET) contained in the multi echoes, the region with multi echoes (RME) is selected and a new scanning trajectory and smaller scanning spot are used to obtain a finer depth image. According to the range and PAET obtained by scanning, the RME is segmented and filled to realize the super-resolution reconstruction of the depth image. By using this method, the experiments of two overlapped plates in space are carried out. By scanning the target with only forty-three points, the super-resolution depth image of the target with 160 × 160 pixels is obtained. Compared with the real depth image of the target, the accuracy of area representation (AOAR) and structural similarity (SSIM) of the reconstructed depth image is 99.89% and 98.94%, respectively. The method proposed in this paper can effectively reduce the number of scanning points and improve the scanning efficiency of the three-dimensional laser imaging system.

scholarly journals Subpixel-Scale Topography Retrieval of Mars Using Single-Image DTM Estimation and Super-Resolution Restoration

2022 ◽  
Vol 14 (2) ◽  
pp. 257
Author(s):  
Yu Tao ◽  
Siting Xiong ◽  
Jan-Peter Muller ◽  
Greg Michael ◽  
Susan J. Conway ◽  
...  
Keyword(s):  
High Resolution ◽  
Mean Squared Error ◽  
Imaging System ◽  
Digital Terrain Model ◽  
Super Resolution ◽  
Landing Site ◽  
Structural Similarity ◽  
Single Image ◽  
Single View ◽  
Imaging Science

We propose using coupled deep learning based super-resolution restoration (SRR) and single-image digital terrain model (DTM) estimation (SDE) methods to produce subpixel-scale topography from single-view ESA Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) and NASA Mars Reconnaissance Orbiter High Resolution Imaging Science Experiment (HiRISE) images. We present qualitative and quantitative assessments of the resultant 2 m/pixel CaSSIS SRR DTM mosaic over the ESA and Roscosmos Rosalind Franklin ExoMars rover’s (RFEXM22) planned landing site at Oxia Planum. Quantitative evaluation shows SRR improves the effective resolution of the resultant CaSSIS DTM by a factor of 4 or more, while achieving a fairly good height accuracy measured by root mean squared error (1.876 m) and structural similarity (0.607), compared to the ultra-high-resolution HiRISE SRR DTMs at 12.5 cm/pixel. We make available, along with this paper, the resultant CaSSIS SRR image and SRR DTM mosaics, as well as HiRISE full-strip SRR images and SRR DTMs, to support landing site characterisation and future rover engineering for the RFEXM22.

scholarly journals Ultra-High-Resolution 1 m/pixel CaSSIS DTM Using Super-Resolution Restoration and Shape-from-Shading: Demonstration over Oxia Planum on Mars

2021 ◽  
Vol 13 (11) ◽  
pp. 2185
Author(s):  
Yu Tao ◽  
Sylvain Douté ◽  
Jan-Peter Muller ◽  
Susan J. Conway ◽  
Nicolas Thomas ◽  
...  
Keyword(s):  
High Resolution ◽  
3D Reconstruction ◽  
Imaging System ◽  
Processing System ◽  
Digital Terrain Model ◽  
Super Resolution ◽  
Landing Site ◽  
Shape From Shading ◽  
Stereo Pair ◽  
Terrain Model

We introduce a novel ultra-high-resolution Digital Terrain Model (DTM) processing system using a combination of photogrammetric 3D reconstruction, image co-registration, image super-resolution restoration, shape-from-shading DTM refinement, and 3D co-alignment methods. Technical details of the method are described, and results are demonstrated using a 4 m/pixel Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) panchromatic image and an overlapping 6 m/pixel Mars Reconnaissance Orbiter Context Camera (CTX) stereo pair to produce a 1 m/pixel CaSSIS Super-Resolution Restoration (SRR) DTM for different areas over Oxia Planum on Mars—the future ESA ExoMars 2022 Rosalind Franklin rover’s landing site. Quantitative assessments are made using profile measurements and the counting of resolvable craters, in comparison with the publicly available 1 m/pixel High-Resolution Imaging Experiment (HiRISE) DTM. These assessments demonstrate that the final resultant 1 m/pixel CaSSIS DTM from the proposed processing system has achieved comparable and sometimes more detailed 3D reconstruction compared to the overlapping HiRISE DTM.

A Video Super Resolution Algorithm Based on Wavelet Coefficient

2014 ◽  
Vol 610 ◽  
pp. 425-428
Author(s):  
Wei Jian Liu ◽  
Si Da Xiao ◽  
Ruo He Yao
Keyword(s):  
High Frequency ◽  
Wavelet Coefficient ◽  
Three Dimensional ◽  
Super Resolution ◽  
Structural Similarity ◽  
Image Sequences ◽  
Discrete Wavelet ◽  
Resolution Image ◽  
Resolution Algorithm ◽  
Visual Results

In this paper, we propose a new super-resolution algorithm based on wavelet coefficient. The proposed algorithm uses discrete wavelet transform (DWT) to decompose the input low-resolution image sequences into four subband images, including LL, LH, HL, HH. Then the input images have been processed by the 3DSKR (Three Dimensional Steering Kernel Regression) super resolution (SR) algorithm, and the result replaces the LL subband image, while the three high-frequency subband images have been interpolated. Finally, combining all these images to generate a new high-resolution image by using inverse DWT. Proposed method has been verified on Calendar and Foliage by Matlab software platform. The peak signal-to-noise (PSNR), structural similarity (SSIM) and visual results are compared, and show that the computational complexity of the proposed algorithm decline by 30 percent compared with the existing algorithm to obtain the approximate results.

scholarly journals A GENERATIVE ADVERSARIAL NETWORK APPROACH FOR SUPER-RESOLUTION OF SENTINEL-2 SATELLITE IMAGES

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 9-14
Author(s):  
F. Pineda ◽  
V. Ayma ◽  
C. Beltran
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Structural Similarity ◽  
Generative Adversarial Network ◽  
Acceptable Result ◽  
Adversarial Network ◽  
High Resolution Satellite Images ◽  
Sentinel 2

Abstract. High-resolution satellite images have always been in high demand due to the greater detail and precision they offer, as well as the wide scope of the fields in which they could be applied; however, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Recent models of convolutional neural networks (CNN) are very suitable for applications with image processing, like resolution enhancement of images; but in order to obtain an acceptable result, it is important, not only to define the kind of CNN architecture but the reference set of images to train the model. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a peruvian satellite, which serve as the reference for the super-resolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR) and the Structural Similarity (SSIM). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well.

scholarly journals Super-Resolution Label-free Volumetric Vibrational Imaging

2021 ◽  
Author(s):  
Chenxi Qian ◽  
Kun Miao ◽  
Li-En Lin ◽  
Xinhong Chen ◽  
Jiajun Du ◽  
...  
Keyword(s):  
High Resolution ◽  
Three Dimensional ◽  
Super Resolution ◽  
Label Free ◽  
Fluorescence Techniques ◽  
Biological Structures ◽  
Imaging Strategy ◽  
Endogenous Proteins ◽  
Low Efficiency ◽  
Protein Rich

Innovations in high-resolution optical imaging have allowed visualization of nanoscale biological structures and connections. However, super-resolution fluorescence techniques, including both optics-oriented and sample-expansion based, are limited in quantification and throughput especially in tissues from photobleaching or quenching of the fluorophores, and low-efficiency or non-uniform delivery of the probes. Here, we report a general sample-expansion vibrational imaging strategy, termed VISTA, for scalable label-free high-resolution interrogations of protein-rich biological structures with resolution down to 82 nm. VISTA achieves decent three-dimensional image quality through optimal retention of endogenous proteins, isotropic sample expansion, and deprivation of scattering lipids. Free from probe-labeling associated issues, VISTA offers unbiased and high-throughput tissue investigations. With correlative VISTA and immunofluorescence, we further validated the imaging specificity of VISTA and trained an image-segmentation model for label-free multi-component and volumetric prediction of nucleus, blood vessels, neuronal cells and dendrites in complex mouse brain tissues. VISTA could hence open new avenues for versatile biomedical studies.

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