scholarly journals Multisensor Remote Sensing Imagery Super-Resolution with Conditional GAN

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Junwei Wang ◽  
Kun Gao ◽  
Zhenzhou Zhang ◽  
Chong Ni ◽  
Zibo Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Real World ◽  
Solution Space ◽  
Super Resolution ◽  
Sensor Data ◽  
Perceptual Quality ◽  
Deep Convolutional Neural Networks ◽  
Generative Adversarial Network ◽  
Remote Sensing Satellite

Despite the promising performance on benchmark datasets that deep convolutional neural networks have exhibited in single image super-resolution (SISR), there are two underlying limitations to existing methods. First, current supervised learning-based SISR methods for remote sensing satellite imagery do not use paired real sensor data, instead operating on simulated high-resolution (HR) and low-resolution (LR) image-pairs (typically HR images with their bicubic-degraded LR counterparts), which often yield poor performance on real-world LR images. Second, SISR is an ill-posed problem, and the super-resolved image from discriminatively trained networks with lp norm loss is an average of the infinite possible HR images, thus, always has low perceptual quality. Though this issue can be mitigated by generative adversarial network (GAN), it is still hard to search in the whole solution-space and find the best solution. In this paper, we focus on real-world application and introduce a new multisensor dataset for real-world remote sensing satellite imagery super-resolution. In addition, we propose a novel conditional GAN scheme for SISR task which can further reduce the solution-space. Therefore, the super-resolved images have not only high fidelity, but high perceptual quality as well. Extensive experiments demonstrate that networks trained on the introduced dataset can obtain better performances than those trained on simulated data. Additionally, the proposed conditional GAN scheme can achieve better perceptual quality while obtaining comparable fidelity over the state-of-the-art methods.

Direct Unsupervised Super-Resolution using Generative Adversarial Network (DUS-GAN) for Real-World Data

2021 ◽  
pp. 1-1
Author(s):  
Kalpesh Prajapati ◽  
Vishal Chudasama ◽  
Heena Patel ◽  
Kishor Upla ◽  
Kiran Raja ◽  
...  
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Real World Data ◽  
Generative Adversarial Network ◽  
World Data ◽  
Adversarial Network

scholarly journals TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution

2021 ◽  
Vol 12 (6) ◽  
pp. 1-20
Author(s):  
Fayaz Ali Dharejo ◽  
Farah Deeba ◽  
Yuanchun Zhou ◽  
Bhagwan Das ◽  
Munsif Ali Jatoi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Single Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Image Super Resolution ◽  
Spatio Temporal ◽  
Single Image Super Resolution

Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR) . However, the generated image still suffers from undesirable artifacts such as the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN). We have introduced a new method incorporating Wavelet Transform (WT) characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using the WT, whereas the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merced Landsat remote sensing dataset and Set14 with each image size of 256 × 256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers.

scholarly journals Generative Adversarial Network-Based Super-Resolution Considering Quantitative and Perceptual Quality

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 449 ◽  
Author(s):  
Can Li ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Naixiang Ao
Keyword(s):  
Super Resolution ◽  
Ground Truth ◽  
Second Order ◽  
Attention Mechanism ◽  
Generative Adversarial Networks ◽  
Perceptual Quality ◽  
Human Visual Perception ◽  
Image Perception ◽  
Generative Adversarial Network ◽  
Image Super Resolution

In recent years, the common algorithms for image super-resolution based on deep learning have been increasingly successful, but there is still a large gap between the results generated by each algorithm and the ground-truth. Even some algorithms that are dedicated to image perception produce more textures that do not exist in the original image, and these artefacts also affect the visual perceptual quality of the image. We believe that in the existing perceptual-based image super-resolution algorithm, it is necessary to consider Super-Resolution (SR) image quality, which can restore the important structural parts of the original picture. This paper mainly improves the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) algorithm in the following aspects: adding a shallow network structure, adding the dual attention mechanism in the generator and the discriminator, including the second-order channel mechanism and spatial attention mechanism and optimizing perceptual loss by adding second-order covariance normalization at the end of feature extractor. The results of this paper ensure image perceptual quality while reducing image distortion and artefacts, improving the perceived similarity of images and making the images more in line with human visual perception.

scholarly journals Small Object Detection in Remote Sensing Images Based on Super-Resolution with Auxiliary Generative Adversarial Networks

2020 ◽  
Vol 12 (19) ◽  
pp. 3152
Author(s):  
Luc Courtrai ◽  
Minh-Tan Pham ◽  
Sébastien Lefèvre
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Network Architecture ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Generative Adversarial Networks ◽  
Small Object ◽  
Remote Sensing Images ◽  
Generative Adversarial Network ◽  
Small Object Detection

This article tackles the problem of detecting small objects in satellite or aerial remote sensing images by relying on super-resolution to increase image spatial resolution, thus the size and details of objects to be detected. We show how to improve the super-resolution framework starting from the learning of a generative adversarial network (GAN) based on residual blocks and then its integration into a cycle model. Furthermore, by adding to the framework an auxiliary network tailored for object detection, we considerably improve the learning and the quality of our final super-resolution architecture, and more importantly increase the object detection performance. Besides the improvement dedicated to the network architecture, we also focus on the training of super-resolution on target objects, leading to an object-focused approach. Furthermore, the proposed strategies do not depend on the choice of a baseline super-resolution framework, hence could be adopted for current and future state-of-the-art models. Our experimental study on small vehicle detection in remote sensing data conducted on both aerial and satellite images (i.e., ISPRS Potsdam and xView datasets) confirms the effectiveness of the improved super-resolution methods to assist with the small object detection tasks.

scholarly journals Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Erick Costa de Farias ◽  
Christian di Noia ◽  
Changhee Han ◽  
Evis Sala ◽  
Mauro Castelli ◽  
...  
Keyword(s):  
Biomarker Discovery ◽  
Super Resolution ◽  
Principal Component ◽  
Medical Decision ◽  
Perceptual Quality ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Proposed Model ◽  
Spatial Pyramid Pooling ◽  
Image Super Resolution

AbstractRobust machine learning models based on radiomic features might allow for accurate diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized radiomic feature extraction has hampered their clinical use. Since the radiomic features tend to be affected by low voxel statistics in regions of interest, increasing the sample size would improve their robustness in clinical studies. Therefore, we propose a Generative Adversarial Network (GAN)-based lesion-focused framework for Computed Tomography (CT) image Super-Resolution (SR); for the lesion (i.e., cancer) patch-focused training, we incorporate Spatial Pyramid Pooling (SPP) into GAN-Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE). At $$2\times $$ 2 × SR, the proposed model achieved better perceptual quality with less blurring than the other considered state-of-the-art SR methods, while producing comparable results at $$4\times $$ 4 × SR. We also evaluated the robustness of our model’s radiomic feature in terms of quantization on a different lung cancer CT dataset using Principal Component Analysis (PCA). Intriguingly, the most important radiomic features in our PCA-based analysis were the most robust features extracted on the GAN-super-resolved images. These achievements pave the way for the application of GAN-based image Super-Resolution techniques for studies of radiomics for robust biomarker discovery.

Sign in / Sign up

Export Citation Format

Share Document