scholarly journals A Super-Resolution and Fusion Approach to Enhancing Hyperspectral Images

2018 ◽  
Vol 10 (9) ◽  
pp. 1416 ◽  
Author(s):  
Chiman Kwan ◽  
Joon Choi ◽  
Stanley Chan ◽  
Jin Zhou ◽  
Bence Budavari
Keyword(s):  
Surface Characterization ◽  
Performance Metrics ◽  
Color Image ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Vegetation Monitoring ◽  
Color Mapping ◽  
Sensing Applications ◽  
Single Image Super Resolution ◽  
Spectral Ranges

High resolution (HR) hyperspectral (HS) images have found widespread applications in terrestrial remote sensing applications, including vegetation monitoring, military surveillance and reconnaissance, fire damage assessment, and many others. They also find applications in planetary missions such as Mars surface characterization. However, resolutions of most HS imagers are limited to tens of meters. Existing resolution enhancement techniques either require additional multispectral (MS) band images or use a panchromatic (pan) band image. The former poses hardware challenges, whereas the latter may have limited performance. In this paper, we present a new resolution enhancement algorithm for HS images that only requires an HR color image and a low resolution (LR) HS image cube. Our approach integrates two newly developed techniques: (1) A hybrid color mapping (HCM) algorithm, and (2) A Plug-and-Play algorithm for single image super-resolution. Comprehensive experiments (objective (five performance metrics), subjective (synthesized fused images in multiple spectral ranges), and pixel clustering) using real HS images and comparative studies with 20 representative algorithms in the literature were conducted to validate and evaluate the proposed method. Results demonstrated that the new algorithm is very promising.

scholarly journals Temporally Consistent Edge-Informed Video Super-Resolution (Edge-VSR)

2021 ◽  
Vol 6 (1) ◽  
pp. 1-6
Author(s):  
Ayush Singh ◽  
Mehran Ebrahimi
Keyword(s):  
Video Sequence ◽  
Structural Information ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Single Image ◽  
Resolution Method ◽  
Edge Information ◽  
Image Super Resolution ◽  
Resolution Algorithm ◽  
Single Image Super Resolution

Resolution enhancement of a given video sequence is known as video super-resolution. We propose an end-to-end trainable video super-resolution method as an extension of the recently developed edge-informed single image super-resolution algorithm. A two-stage adversarial-based convolutional neural network that incorporates temporal information along with the current frame's structural information will be used. The edge information in each frame along with optical flow technique for motion estimation among frames will be applied. Promising results on validation datasets will be presented.

scholarly journals Sparse Representation Based Super-Resolution Algorithm using Wavelet Domain Interpolation and Nonlocal Means

2015 ◽  
Vol 16 (2) ◽  
pp. 296
Author(s):  
Gunnam Suryanarayana ◽  
Ravindra Dhuli
Keyword(s):  
Resolution Enhancement ◽  
Super Resolution ◽  
Image Resolution ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Nonlocal Means ◽  
Image Super Resolution ◽  
Resolution Algorithm ◽  
Single Image Super Resolution ◽  
Enhancement Algorithm

In this correspondence, we propose a novel image resolution enhancement algorithm based on discretewavelet transform (DWT), stationary wavelet transform (SWT) and sparse signal recovery of the inputimage. The nonlocal means filter is employed in the preliminary denoising stage of the proposed method.The denoised input low resolution (LR) image is then decomposed into different frequency subbands byemploying DWT and SWT simultaneously. In parallel, the denoised LR image is subjected to a sparse signalrepresentation based interpolation. All the estimated high frequency subbands as well as the sparseinterpolated LR image are fused to generate a high resolution (HR) image by using inverse discrete wavelettransform (IDWT). Experimental results on various test images show the superiority of our method over theconventional and state-of-the-art single image super- resolution (SR) techniques in achieving the real timeperformance.

scholarly journals Single-Image Super-Resolution Using Panchromatic Gradient Prior and Variational Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yingying Xu ◽  
Jianhua Li ◽  
Haifeng Song ◽  
Lei Du
Keyword(s):  
Resolution Enhancement ◽  
Super Resolution ◽  
Low Rank ◽  
Variational Model ◽  
Single Image ◽  
Evaluation Indexes ◽  
Ill Posed ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Image Edges

Single-image super-resolution (SISR) is a resolution enhancement technique and is known as an ill-posed problem. Motivated by the idea of pan-sharping, we propose a novel variational model for SISR. The structure tensor of the input low-resolution image is exploited to obtain the gradient of an imaginary panchromatic image. Then, by constraining the gradient consistency, the image edges and details can be better recovered during the procedure of restoration of high-resolution images. Besides, we resort to the nonlocal sparse and low-rank regularization of image patches to further improve the super-resolution performance. The proposed variational model is efficiently solved by ADMM-based algorithm. We do extensive experiments in natural images and remote sensing images with different magnifying factors and compare our method with three classical super-resolution methods. The subjective visual impression and quantitative evaluation indexes both show that our method can obtain higher-quality results.

scholarly journals Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network

2021 ◽  
Vol 18 (11) ◽  
pp. 5890
Author(s):  
Qiang Yu ◽  
Feiqiang Liu ◽  
Long Xiao ◽  
Zitao Liu ◽  
Xiaomin Yang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Super Resolution ◽  
Model Complexity ◽  
Practical Application ◽  
Single Image ◽  
Feature Maps ◽  
Benchmark Datasets ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden layers in convolutional neural networks (CNN) help the model learn useful information. However, there exists redundancy among feature maps, which can be further exploited. To address these issues, this paper proposes a lightweight efficient feature generating network (EFGN) for SISR by constructing the efficient feature generating block (EFGB). Specifically, the EFGB can conduct plain operations on the original features to produce more feature maps with parameters slightly increasing. With the help of these extra feature maps, the network can extract more useful information from low resolution (LR) images to reconstruct the desired high resolution (HR) images. Experiments conducted on the benchmark datasets demonstrate that the proposed EFGN can outperform other deep-learning based methods in most cases and possess relatively lower model complexity. Additionally, the running time measurement indicates the feasibility of real-time monitoring.

Compact and progressive network for enhanced single image super-resolution—ComPrESRNet

2021 ◽  
Author(s):  
Vishal Chudasama ◽  
Kishor Upla ◽  
Kiran Raja ◽  
Raghavendra Ramachandra ◽  
Christoph Busch
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution
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