scholarly journals Single-shot and single-sensor high/super-resolution microwave imaging based on metasurface

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Libo Wang ◽  
Lianlin Li ◽  
Yunbo Li ◽  
Hao Chi Zhang ◽  
Tie Jun Cui
Keyword(s):  
Super Resolution ◽  
Microwave Imaging ◽  
Single Shot ◽  
Single Sensor

scholarly journals Single-shot super-resolution phase imaging by wavefront separation approach

2021 ◽  
Author(s):  
Péter Kocsis ◽  
Igor Shevkunov ◽  
Vladimir Katkovnik ◽  
Heikki Rekola ◽  
Karen Egiazarian
Keyword(s):  
Super Resolution ◽  
Phase Imaging ◽  
Single Shot

scholarly journals Mosaic Super-resolution via Sequential Feature Pyramid Networks

2019 ◽  
Author(s):  
Mehrdad Shoeiby ◽  
Mohammad Ali Armin ◽  
Sadegh Aliakbarian ◽  
Saeed Anwar ◽  
Lars petersson
Keyword(s):  
State Of The Art ◽  
Super Resolution ◽  
Autonomous Driving ◽  
Single Shot ◽  
Current State ◽  
Wide Range ◽  
Feature Pyramid ◽  
Novel Method ◽  
Convolutional Lstm ◽  
Mosaic Images

<div>Advances in the design of multi-spectral cameras have</div><div>led to great interests in a wide range of applications, from</div><div>astronomy to autonomous driving. However, such cameras</div><div>inherently suffer from a trade-off between the spatial and</div><div>spectral resolution. In this paper, we propose to address</div><div>this limitation by introducing a novel method to carry out</div><div>super-resolution on raw mosaic images, multi-spectral or</div><div>RGB Bayer, captured by modern real-time single-shot mo-</div><div>saic sensors. To this end, we design a deep super-resolution</div><div>architecture that benefits from a sequential feature pyramid</div><div>along the depth of the network. This, in fact, is achieved</div><div>by utilizing a convolutional LSTM (ConvLSTM) to learn the</div><div>inter-dependencies between features at different receptive</div><div>fields. Additionally, by investigating the effect of different</div><div>attention mechanisms in our framework, we show that a</div><div>ConvLSTM inspired module is able to provide superior at-</div><div>tention in our context. Our extensive experiments and anal-</div><div>yses evidence that our approach yields significant super-</div><div>resolution quality, outperforming current state-of-the-art</div><div>mosaic super-resolution methods on both Bayer and multi-</div><div>spectral images. Additionally, to the best of our knowledge,</div><div>our method is the first specialized method to super-resolve</div><div>mosaic images, whether it be multi-spectral or Bayer.</div><div><br></div>

Foundation of Super-Resolution Microwave Imaging

2017 ◽  
pp. 429-443
Author(s):  
Ruliang Yang ◽  
Haiying Li ◽  
Shiqiang Li ◽  
Ping Zhang ◽  
Lulu Tan ◽  
...  
Keyword(s):  
Super Resolution ◽  
Microwave Imaging

scholarly journals Transmission-Type 2-Bit Programmable Metasurface for Single-Sensor and Single-Frequency Microwave Imaging

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Yun Bo Li ◽  
Lian Lin Li ◽  
Bai Bing Xu ◽  
Wei Wu ◽  
Rui Yuan Wu ◽  
...  
Keyword(s):  
Microwave Imaging ◽  
Single Frequency ◽  
Single Sensor

scholarly journals Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

2020 ◽  
Author(s):  
Jakaria Rabbi ◽  
Nilanjan Ray ◽  
Matthias Schubert ◽  
Subir Chowdhury ◽  
Dennis Chao
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Super Resolution ◽  
Detection Performance ◽  
Superior Performance ◽  
Single Shot ◽  
Small Object ◽  
Remote Sensing Images ◽  
Low Resolution ◽  
End To End

The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for the small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and used different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the GAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on car overhead with context and oil and gas storage tank (created by us) data sets show superior performance of our method compared to the standalone state-of-the-art object detectors.

scholarly journals Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks

2019 ◽  
Vol 11 (22) ◽  
pp. 2635 ◽  
Author(s):  
Massimiliano Gargiulo ◽  
Antonio Mazza ◽  
Raffaele Gaetano ◽  
Giuseppe Ruello ◽  
Giuseppe Scarpa
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Near Infrared ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Limiting Factor ◽  
Considerable Saving ◽  
Access Policy ◽  
Single Sensor ◽  
Sentinel 2

Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of information for the entire remote sensing community, thanks to their unprecedented combination of spatial, spectral and temporal resolution, as well as their associated open access policy. Due to a sensor design trade-off, images are acquired (and delivered) at different spatial resolutions (10, 20 and 60 m) according to specific sets of wavelengths, with only the four visible and near infrared bands provided at the highest resolution (10 m). Although this is not a limiting factor in general, many applications seem to emerge in which the resolution enhancement of 20 m bands may be beneficial, motivating the development of specific super-resolution methods. In this work, we propose to leverage Convolutional Neural Networks (CNNs) to provide a fast, upscalable method for the single-sensor fusion of Sentinel-2 (S2) data, whose aim is to provide a 10 m super-resolution of the original 20 m bands. Experimental results demonstrate that the proposed solution can achieve better performance with respect to most of the state-of-the-art methods, including other deep learning based ones with a considerable saving of computational burden.

scholarly journals Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

2020 ◽  
Author(s):  
Jakaria Rabbi ◽  
Nilanjan Ray ◽  
Matthias Schubert ◽  
Subir Chowdhury ◽  
Dennis Chao
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Super Resolution ◽  
Detection Performance ◽  
Superior Performance ◽  
Single Shot ◽  
Small Object ◽  
Remote Sensing Images ◽  
Low Resolution ◽  
End To End

The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) and a self-assembled (oil and gas storage tank) satellite dataset show superior performance of our method compared to the standalone state-of-the-art object detectors.

scholarly journals Residual Super-Resolution Single Shot Network for Low-Resolution Object Detection

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 47780-47793 ◽  
Author(s):  
Xiaotong Zhao ◽  
Wei Li ◽  
Yifan Zhang ◽  
Zhiyong Feng
Keyword(s):  
Object Detection ◽  
Super Resolution ◽  
Single Shot ◽  
Low Resolution ◽  
Resolution Single
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