Single Space Object Image Denoising and Super-Resolution Reconstructing Using Deep Convolutional Networks

Feng, Xiao; Su, Xiuqin; Shen, Junge; Jin, Humin

doi:10.3390/rs11161910

Cited by 13 publications

(8 citation statements)

References 32 publications

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“…In our generator, residual learning and dense connection are the most important implementations. Residual learning is learning the residual between the input and output, so in this article, we define a residual r = y − x, most of which may be zero or less [14]. In this equation, r is the residual pixel of x and y.…”

Section: Network Architecturementioning

confidence: 99%

“…In recent years, with the development of big data technology, machine learning methods have become increasingly popular and practical. Deep learning methods, which usually mean deep convolutional neural networks (CNN) [12][13][14], are currently one of the research hot-spots. Deep learning methods have achieved impressive results in many fields such as image processing.…”

Section: Introductionmentioning

confidence: 99%

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Optical Remote Sensing Image Denoising and Super-Resolution Reconstructing Using Optimized Generative Network in Wavelet Transform Domain

Feng

Zhang

et al. 2021

Remote Sensing

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High spatial quality (HQ) optical remote sensing images are very useful for target detection, target recognition and image classification. Due to the influence of imaging equipment accuracy and atmospheric environment, HQ images are difficult to acquire, while low spatial quality (LQ) remote sensing images are very easy to acquire. Hence, denoising and super-resolution (SR) reconstruction technology are the most important solutions to improve the quality of remote sensing images very effectively, which can lower the cost as much as possible. Most existing methods usually only employ denoising or SR technology to obtain HQ images. However, due to the complex structure and the large noise of remote sensing images, the quality of the remote sensing image obtained only by denoising method or SR method cannot meet the actual needs. To address these problems, a method of reconstructing HQ remote sensing images based on Generative Adversarial Network (GAN) named “Restoration Generative Adversarial Network with ResNet and DenseNet” (RRDGAN) is proposed, which can acquire better quality images by incorporating denoising and SR into a unified framework. The generative network is implemented by fusing Residual Neural Network (ResNet) and Dense Convolutional Network (DenseNet) in order to consider denoising and SR problems at the same time. Then, total variation (TV) regularization is used to furthermore enhance the edge details, and the idea of Relativistic GAN is explored to make the whole network converge better. Our RRDGAN is implemented in wavelet transform (WT) domain, since different frequency parts could be handled separately in the wavelet domain. The experimental results on three different remote sensing datasets shows the feasibility of our proposed method in acquiring remote sensing images.

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Section: Network Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optical Remote Sensing Image Denoising and Super-Resolution Reconstructing Using Optimized Generative Network in Wavelet Transform Domain

Feng

Zhang

et al. 2021

Remote Sensing

Self Cite

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“…A typical CNN consists of four layers: convolutional, activation, pooling, and fully connected. Convolutional layers have the characteristics of sparse local connections and weight sharing, using moving convolution filters that significantly reduce the number of network parameters and improve efficiency [43]. A weightshared convolution layer is usually followed by a nonlinear activation layer to capture more complex characteristics of the input data.…”

Section: B Multi-model Neural Networkmentioning

confidence: 99%

Remote Sensing and Social Sensing Data Fusion for Fine-Resolution Population Mapping With a Multimodel Neural Network

Cheng

Wang

Feng

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Mapping population distribution at fine spatial scales is significant and fundamental to solve resource utilization, assessment of city disaster, environmental regulation, and urbanization. Multi-source data produced by remote and social sensing have been widely used to disaggregate census information to map population distributions at fine resolution. However, it is challenging to achieve accurate high spatial resolution population mapping by combining multi-source data and considering geographic spatial heterogeneity. The existing approaches do not consider global and local spatial information simultaneously, resulting in low accuracy. This paper proposes a multi-model fusion neural network for estimating fine-resolution population estimates from multi-source data. Our approach takes into account the local spatial information and global information of each geographic unit. Specifically, a first-order space matrix of a geographic unit is used to characterize its local spatial information. We proposed a multi-model neural network, which combines a convolutional neural network (CNN) and a multilayer perceptron (MLP) model to estimate a fine-resolution population mapping. Using Shenzhen, China, as the experimental setting, a population distribution map was generated at 100m spatial resolution. The model was quantitatively validated by showing that it captured the relationship between estimated population and census population at township level (R 2 = 0.77) more accurately than the WorldPop dataset (R 2 = 0.51) and the MLPbased model (R 2 = 0.63). Qualitatively, the proposed model can identify differences in population density in densely populated areas and some remote population clusters more accurately than the WorldPop populcation dataset.

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“…In previous works, methods of image SR can be roughly divided into two categories: traditional methods [17,33,34] and deep learning-based methods [18,19,35,36]. Due to the limitation of space, we only briefly review the works related to deep learning networks for single image super-resolution, attention mechanism, and perceptual optimization.…”

Section: Related Workmentioning

confidence: 99%

URNet: A U-Shaped Residual Network for Lightweight Image Super-Resolution

et al. 2021

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It is extremely important and necessary for low computing power or portable devices to design more lightweight algorithms for image super-resolution (SR). Recently, most SR methods have achieved outstanding performance by sacrificing computational cost and memory storage, or vice versa. To address this problem, we introduce a lightweight U-shaped residual network (URNet) for fast and accurate image SR. Specifically, we propose a more effective feature distillation pyramid residual group (FDPRG) to extract features from low-resolution images. The FDPRG can effectively reuse the learned features with dense shortcuts and capture multi-scale information with a cascaded feature pyramid block. Based on the U-shaped structure, we utilize a step-by-step fusion strategy to improve the performance of feature fusion of different blocks. This strategy is different from the general SR methods which only use a single Concat operation to fuse the features of all basic blocks. Moreover, a lightweight asymmetric residual non-local block is proposed to model the global context information and further improve the performance of SR. Finally, a high-frequency loss function is designed to alleviate smoothing image details caused by pixel-wise loss. Simultaneously, the proposed modules and high-frequency loss function can be easily plugged into multiple mature architectures to improve the performance of SR. Extensive experiments on multiple natural image datasets and remote sensing image datasets show the URNet achieves a better trade-off between image SR performance and model complexity against other state-of-the-art SR methods.

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Single Space Object Image Denoising and Super-Resolution Reconstructing Using Deep Convolutional Networks

Cited by 13 publications

References 32 publications

Optical Remote Sensing Image Denoising and Super-Resolution Reconstructing Using Optimized Generative Network in Wavelet Transform Domain

Optical Remote Sensing Image Denoising and Super-Resolution Reconstructing Using Optimized Generative Network in Wavelet Transform Domain

Remote Sensing and Social Sensing Data Fusion for Fine-Resolution Population Mapping With a Multimodel Neural Network

URNet: A U-Shaped Residual Network for Lightweight Image Super-Resolution

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