SUPER RESOLUTION RECONSTRUCTION BASED ON ADAPTIVE DETAIL ENHANCEMENT FOR ZY-3 SATELLITE IMAGES

Super-resolution reconstruction of sequence remote sensing image is a technology which handles multiple low-resolution satellite remote sensing images with complementary information and obtains one or more high resolution images. The cores of the technology are high precision matching between images and high detail information extraction and fusion. In this paper puts forward a new image super resolution model frame which can adaptive multi-scale enhance the details of reconstructed image. First, the sequence images were decomposed into a detail layer containing the detail information and a smooth layer containing the large scale edge information by bilateral filter. Then, a texture detail enhancement function was constructed to promote the magnitude of the medium and small details. Next, the non-redundant information of the super reconstruction was obtained by differential processing of the detail layer, and the initial super resolution construction result was achieved by interpolating fusion of non-redundant information and the smooth layer. At last, the final reconstruction image was acquired by executing a local optimization model on the initial constructed image. Experiments on ZY-3 satellite images of same phase and different phase show that the proposed method can both improve the information entropy and the image details evaluation standard comparing with the interpolation method, traditional TV algorithm and MAP algorithm, which indicate that our method can obviously highlight image details and contains more ground texture information. A large number of experiment results reveal that the proposed method is robust and universal for different kinds of ZY-3 satellite images.

Download Full-text

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

Sensors ◽

10.3390/s18020498 ◽

2018 ◽

Vol 18 (2) ◽

pp. 498 ◽

Cited By ~ 12

Author(s):

Hong Zhu ◽

Xinming Tang ◽

Junfeng Xie ◽

Weidong Song ◽

Fan Mo ◽

...

Keyword(s):

Remote Sensing ◽

Super Resolution ◽

Remote Sensing Images ◽

Multi Scale ◽

Spatio Temporal ◽

Detail Enhancement

Download Full-text

Image Super Resolution Reconstruction Based on Improved Invertible Rescaling Net

10.21203/rs.3.rs-545193/v1 ◽

2021 ◽

Author(s):

Taiping Mo ◽

Dehong Chen

Keyword(s):

Feature Extraction ◽

High Frequency ◽

Interpolation Method ◽

Super Resolution ◽

Frequency Information ◽

Ill Posed ◽

Image Edge ◽

Image Super Resolution ◽

Improved Model ◽

Image Details

Abstract The Invertible Rescaling Net (IRN) is modeling image downscaling and upscaling as a unified task to alleviate the ill-posed problem in the super-resolution task. However, the ability of potential variables of the model embedded high-frequency information is general, which affects the performance of the reconstructed image. In order to improve the ability of embedding high-frequency information and further reduce the complexity of the model, the potential variables and feature extraction of key components of IRN are improved. Attention mechanism and dilated convolution are used to improve the feature extraction block, reduce the parameters of feature extraction block, and allocate more attention to the image details. The high frequency sub-band interpolation method of wavelet domain is used to improve the potential variables, process and save the image edge, and enhance the ability of embedding high frequency information. Experimental results show that compared with IRN model, improved model has less complexity and excellent performance.

Download Full-text

Deep Distillation Recursive Network for Remote Sensing Imagery Super-Resolution

Remote Sensing ◽

10.3390/rs10111700 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1700 ◽

Cited By ~ 26

Author(s):

Kui Jiang ◽

Zhongyuan Wang ◽

Peng Yi ◽

Junjun Jiang ◽

Jing Xiao ◽

...

Keyword(s):

Remote Sensing ◽

Video Processing ◽

Satellite Images ◽

State Of The Art ◽

Satellite Image ◽

Super Resolution ◽

Poor Performance ◽

Feature Maps ◽

Deep Convolutional Neural Networks ◽

Frequency Components

Deep convolutional neural networks (CNNs) have been widely used and achieved state-of-the-art performance in many image or video processing and analysis tasks. In particular, for image super-resolution (SR) processing, previous CNN-based methods have led to significant improvements, when compared with shallow learning-based methods. However, previous CNN-based algorithms with simple direct or skip connections are of poor performance when applied to remote sensing satellite images SR. In this study, a simple but effective CNN framework, namely deep distillation recursive network (DDRN), is presented for video satellite image SR. DDRN includes a group of ultra-dense residual blocks (UDB), a multi-scale purification unit (MSPU), and a reconstruction module. In particular, through the addition of rich interactive links in and between multiple-path units in each UDB, features extracted from multiple parallel convolution layers can be shared effectively. Compared with classical dense-connection-based models, DDRN possesses the following main properties. (1) DDRN contains more linking nodes with the same convolution layers. (2) A distillation and compensation mechanism, which performs feature distillation and compensation in different stages of the network, is also constructed. In particular, the high-frequency components lost during information propagation can be compensated in MSPU. (3) The final SR image can benefit from the feature maps extracted from UDB and the compensated components obtained from MSPU. Experiments on Kaggle Open Source Dataset and Jilin-1 video satellite images illustrate that DDRN outperforms the conventional CNN-based baselines and some state-of-the-art feature extraction approaches.

Download Full-text

Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution Remote Sensing Images

Remote Sensing ◽

10.3390/rs13061104 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1104

Author(s):

Yuanfu Gong ◽

Puyun Liao ◽

Xiaodong Zhang ◽

Lifei Zhang ◽

Guanzhou Chen ◽

...

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Super Resolution ◽

The Other ◽

Generative Adversarial Networks ◽

Remote Sensing Images ◽

Adversarial Networks ◽

Large Size ◽

Internal Inconsistency ◽

Sentinel 2

Previously, generative adversarial networks (GAN) have been widely applied on super resolution reconstruction (SRR) methods, which turn low-resolution (LR) images into high-resolution (HR) ones. However, as these methods recover high frequency information with what they observed from the other images, they tend to produce artifacts when processing unfamiliar images. Optical satellite remote sensing images are of a far more complicated scene than natural images. Therefore, applying the previous networks on remote sensing images, especially mid-resolution ones, leads to unstable convergence and thus unpleasing artifacts. In this paper, we propose Enlighten-GAN for SRR tasks on large-size optical mid-resolution remote sensing images. Specifically, we design the enlighten blocks to induce network converging to a reliable point, and bring the Self-Supervised Hierarchical Perceptual Loss to attain performance improvement overpassing the other loss functions. Furthermore, limited by memory, large-scale images need to be cropped into patches to get through the network separately. To merge the reconstructed patches into a whole, we employ the internal inconsistency loss and cropping-and-clipping strategy, to avoid the seam line. Experiment results certify that Enlighten-GAN outperforms the state-of-the-art methods in terms of gradient similarity metric (GSM) on mid-resolution Sentinel-2 remote sensing images.

Download Full-text

A New Super Resolution Framework Based on Multi-Task Learning for Remote Sensing Images

Sensors ◽

10.3390/s21051743 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1743

Author(s):

Li Yan ◽

Kun Chang

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Learning Strategy ◽

Super Resolution ◽

Remote Sensing Images ◽

Unknown Parameters ◽

Learning Module ◽

Task Learning ◽

Blur Kernel ◽

Gaussian Blur

Super-resolution (SR) algorithms based on deep learning have dominated in various tasks, including medical imaging, street view surveillance and face recognition. In the remote sensing field, most of the current SR methods utilize the low-resolution (LR) images that directly bicubic downsampled the high-resolution (HR) images as not only train set but also test set, thus achieving high PSNR/SSIM scores but showing performance drop in application because the degradation model in remote sensing images is subjected to Gaussian blur with unknown parameters. Inspired by multi-task learning strategy, we propose a multiple-blur-kernel super-resolution framework (MSF), in which a multiple-blur-kernel learning module (MLM) optimizes the parameters of the network transferable and sensitive for SR procedures with different blur kernels. Besides, to simultaneously exploit the prior of the large-scale remote sensing images and recurrent information in a single test image, a class-feature capture module (CCM) and an unsupervised learning module (ULM) are leveraged in our framework. Extensive experiments show that our framework outperforms the current state-of-the-art SR algorithms in remotely sensed imagery SR with unknown Gaussian blur kernel.

Download Full-text

Improved Wallis Dodging Algorithm for Large-Scale Super-Resolution Reconstruction Remote Sensing Images

Sensors ◽

10.3390/s17030623 ◽

2017 ◽

Vol 17 (3) ◽

pp. 623 ◽

Cited By ~ 5

Author(s):

Chong Fan ◽

Xushuai Chen ◽

Lei Zhong ◽

Min Zhou ◽

Yun Shi ◽

...

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Super Resolution ◽

Remote Sensing Images

Download Full-text

OPPORTUNITIES FOR THE USE OF OPEN REMOTE SENSING DATA FOR MAPPING THE DYNAMICS OF THE BANKS OF RESERVOIRS

Interexpo GEO-Siberia ◽

10.33764/2618-981x-2019-1-2-172-177 ◽

2019 ◽

Vol 1 (2) ◽

pp. 172-177

Author(s):

Vladimir Stupin ◽

Leonid Plastinin

Keyword(s):

Remote Sensing ◽

Open Access ◽

Large Scale ◽

Satellite Images ◽

Remote Sensing Data ◽

Limited Information ◽

Sensing Data ◽

Space Data ◽

Short Time

The article presents the results of an analysis of the possibilities of practical use of open-access space data for regionalization and mapping of types of shores of the reservoirs of the Angara cascade. The principles of coast typing and their interpretive features on medium- and large-scale publicly available satellite images are considered. The described experience may be useful if it is necessary to map the dynamics of extended coastal lines in a short time and with limited information capabilities.

Download Full-text

Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network

Remote Sensing ◽

10.3390/rs11131588 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1588 ◽

Cited By ~ 13

Author(s):

Tao Lu ◽

Jiaming Wang ◽

Yanduo Zhang ◽

Zhongyuan Wang ◽

Junjun Jiang

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Frequency ◽

Satellite Images ◽

Satellite Image ◽

Super Resolution ◽

Object Identification ◽

Remote Sensing Images ◽

Frequency Information ◽

Multi Scale

Recently, the application of satellite remote sensing images is becoming increasingly popular, but the observed images from satellite sensors are frequently in low-resolution (LR). Thus, they cannot fully meet the requirements of object identification and analysis. To utilize the multi-scale characteristics of objects fully in remote sensing images, this paper presents a multi-scale residual neural network (MRNN). MRNN adopts the multi-scale nature of satellite images to reconstruct high-frequency information accurately for super-resolution (SR) satellite imagery. Different sizes of patches from LR satellite images are initially extracted to fit different scale of objects. Large-, middle-, and small-scale deep residual neural networks are designed to simulate differently sized receptive fields for acquiring relative global, contextual, and local information for prior representation. Then, a fusion network is used to refine different scales of information. MRNN fuses the complementary high-frequency information from differently scaled networks to reconstruct the desired high-resolution satellite object image, which is in line with human visual experience (“look in multi-scale to see better”). Experimental results on the SpaceNet satellite image and NWPU-RESISC45 databases show that the proposed approach outperformed several state-of-the-art SR algorithms in terms of objective and subjective image qualities.

Download Full-text

A robust combination interpolation method for video super-resolution

Science and Technology Development Journal ◽

10.32508/stdj.v16i3.1609 ◽

2013 ◽

Vol 16 (3) ◽

pp. 41-57

Author(s):

Cao Thu Bui ◽

Thuong Tien Le ◽

Tuan Hong Do ◽

Hoang Duc Nguyen

Keyword(s):

Efficient Method ◽

Interpolation Method ◽

Super Resolution ◽

Low Complexity ◽

Border Regions ◽

Video Frames ◽

Image Details ◽

Component Image ◽

Better Than

This paper presents an efficient method for video super-resolution (SR) based on two main ideals: Firstly, input video frames can be separated into two components, nontexturing image and texturing image. Then each component image is applied to a compatible interpolation method to improve the quality of high-resolution (HR) reconstructed frame. Secondly, based on the approach that border regions of image details are the most lossy information regions from the sampling process. Therefore, a task of compensation interpolation is essential to increase the quality of the reconstructed HR images. From these discussions, we proposed an efficient method for video SR by combining the spatial interpolation in different texturing regions and the sampling compensation interpolation to improve the quality of video super-resolution. Our results shown that, the quality of HR frames, reconstructed by the proposed method, is better than that of other methods, , and in recently. The significant contribution is the low complexity of the proposed method. Hence, it is possible to apply the proposed algorithm to real-time video super-resolution applications.

Download Full-text