Super-resolution enhancement of flash LADAR range data

2007 ◽  
Author(s):  
Gavin Rosenbush ◽  
Tsai Hong ◽  
Roger D. Eastman
Keyword(s):  
Resolution Enhancement ◽  
Super Resolution ◽  
Range Data

scholarly journals Two-dimensional TIRF-SIM–traction force microscopy (2D TIRF-SIM-TFM)

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Liliana Barbieri ◽  
Huw Colin-York ◽  
Kseniya Korobchevskaya ◽  
Di Li ◽  
Deanna L. Wolfson ◽  
...  
Keyword(s):  
Resolution Enhancement ◽  
Traction Force ◽  
Super Resolution ◽  
Traction Force Microscopy ◽  
Two Dimensional ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Force Microscopy ◽  
Health And Disease ◽  
Spatio Temporal

AbstractQuantifying small, rapidly evolving forces generated by cells is a major challenge for the understanding of biomechanics and mechanobiology in health and disease. Traction force microscopy remains one of the most broadly applied force probing technologies but typically restricts itself to slow events over seconds and micron-scale displacements. Here, we improve >2-fold spatially and >10-fold temporally the resolution of planar cellular force probing compared to its related conventional modalities by combining fast two-dimensional total internal reflection fluorescence super-resolution structured illumination microscopy and traction force microscopy. This live-cell 2D TIRF-SIM-TFM methodology offers a combination of spatio-temporal resolution enhancement relevant to forces on the nano- and sub-second scales, opening up new aspects of mechanobiology to analysis.

scholarly journals Small Object Detection in Remote Sensing Images with Residual Feature Aggregation-Based Super-Resolution and Object Detector Network

2021 ◽  
Vol 13 (9) ◽  
pp. 1854
Author(s):  
Syed Muhammad Arsalan Bashir ◽  
Yi Wang
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Detection Performance ◽  
Image Resolution ◽  
Small Object ◽  
Remote Sensing Images ◽  
Feature Aggregation ◽  
Image Super Resolution

This paper deals with detecting small objects in remote sensing images from satellites or any aerial vehicle by utilizing the concept of image super-resolution for image resolution enhancement using a deep-learning-based detection method. This paper provides a rationale for image super-resolution for small objects by improving the current super-resolution (SR) framework by incorporating a cyclic generative adversarial network (GAN) and residual feature aggregation (RFA) to improve detection performance. The novelty of the method is threefold: first, a framework is proposed, independent of the final object detector used in research, i.e., YOLOv3 could be replaced with Faster R-CNN or any object detector to perform object detection; second, a residual feature aggregation network was used in the generator, which significantly improved the detection performance as the RFA network detected complex features; and third, the whole network was transformed into a cyclic GAN. The image super-resolution cyclic GAN with RFA and YOLO as the detection network is termed as SRCGAN-RFA-YOLO, which is compared with the detection accuracies of other methods. Rigorous experiments on both satellite images and aerial images (ISPRS Potsdam, VAID, and Draper Satellite Image Chronology datasets) were performed, and the results showed that the detection performance increased by using super-resolution methods for spatial resolution enhancement; for an IoU of 0.10, AP of 0.7867 was achieved for a scale factor of 16.

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 A technique for resolution assessment in blind-SIM experiments

2021 ◽  
Author(s):  
Imen Boujmil ◽  
Giancarlo Ruocco ◽  
Marco Leonetti
Keyword(s):  
High Resolution ◽  
Biological Sample ◽  
A Priori ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Experimental Setup ◽  
Wide Field ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Resolution Measurement

Super resolution techniques are an excellent alternative to wide field microscopy, providing high resolution also in (typically fragile) biological sample. Among the various super resolution techniques, Structured Illumination Microscopy (SIM) improve resolution by employing multiple illumination patterns to be deconvolved with a dedicated software. In the case of blind SIM techniques, unknown patterns, such as speckles, are used, thus providing super resolved images, nearly unaffected by aberrations with a simplified experimental setup. Scattering Assisted Imaging, a special blind SIM technique, exploits an illumination PSF (speckle grains size), smaller than the collection PSF (defined by the collection objectives), to surpass the typical SIM resolution enhancement. However, if SAI is used, it is very difficult to extract the resolution enhancement form a priori considerations. In this paper we propose a protocol and experimental setup for the resolution measurement, demonstrating the resolution enhancement for different collection PSF values.

Block unshifting high-accuracy motion estimation: A new method adapted to super-resolution enhancement

2018 ◽  
Vol 65 ◽  
pp. 81-93
Author(s):  
Konstantinos Konstantoudakis ◽  
Lazaros Vrysis ◽  
Nikolaos Tsipas ◽  
Charalampos Dimoulas
Keyword(s):  
Motion Estimation ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
High Accuracy ◽  
New Method

An overview of structured illumination microscopy: recent advances and perspectives

2021 ◽  
Author(s):  
Krishnendu Samanta ◽  
Joby Joseph
Keyword(s):  
Spatial Frequency ◽  
Basic Principle ◽  
Imaging Techniques ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Diffraction Limit ◽  
Structured Illumination ◽  
Post Processing ◽  
Structured Illumination Microscopy ◽  
Near Future

Abstract Structured illumination microscopy (SIM) is one of the most significant widefield super-resolution optical imaging techniques. The conventional SIM utilizes a sinusoidal structured pattern to excite the fluorescent sample; which eventually down-modulates higher spatial frequency sample information within the diffraction-limited passband of the microscopy system and provides around two-fold resolution enhancement over diffraction limit after suitable computational post-processing. Here we provide an overview of the basic principle, image reconstruction, technical development of the SIM technique. Nonetheless, in order to push the SIM resolution further towards the extreme nanoscale dimensions, several different approaches are launched apart from the conventional SIM. Among the various SIM methods, some of the important techniques e.g. TIRF, non-linear, plasmonic, speckle SIM etc. are discussed elaborately. Moreover, we highlight different implementations of SIM in various other imaging modalities to enhance their imaging performances with augmented capabilities. Finally, some future outlooks are mentioned which might develop fruitfully and pave the way for new discoveries in near future.

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