Generative Adversarial Network for Deblurring of Remote Sensing Image

Zhang, Yungang; Xiang, Yu; Lei, Baiying

doi:10.1109/geoinformatics.2018.8557110

Cited by 8 publications

(15 citation statements)

References 10 publications

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“…Each DRB block contains three 3 × 3 convolution layers with a batch normalisation layer. Consider an l th convolution layer output

x_{l}

as input of DRB and

r(\cdot )

be a residual mapping function, then each DRB block can be presented mathematically as [123],

\begin{eqnarray} R&=&r{\left(x_{l},W_{i}\right)}+x_{l}\nonumber\\ r{\left(x_{l},W_{i}\right)} &=& rW_{3}{\left[rW_{2}{\left(rW_{1}{\left(x_{l}\right)}\right)}\right]}\nonumber\\ D &=& F{\left({\left[x_{l},x_{l+1},x_{l+2},x_{l+3},R\right]}\right)}\nonumber\\ O&=&H{\left(D\right)} \end{eqnarray}

where O is the dense residual network block output, R and D are the outputs of residual and dense connections respectively,

([x_{l},x_{l+1},x_{l+2},x_{l+3},R])

represents the concatenation of feature maps in layers

(l,\ldots ,l+3)

and residual output

$R, r$<...>

…”

Section: Proposed Methodsmentioning

confidence: 99%

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Multi‐scale GAN with residual image learning for removing heterogeneous blur

Khan

Luo

2022

IET Image Processing

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Processing images with heterogeneous blur remains challenging due to multiple degradation aspects that could affect structural properties. This study proposes a deep learning‐based multi‐scaled generative adversarial network (GAN) with residual image learning to process variant and in‐variant blur. Different scaled images with corresponding gradients are concatenated as a multi‐channel single input for the proposed GAN. Residual‐ and dense‐networks are combined to explore salient features in the bottleneck section while addressing the vanishing gradient problem. A hybrid content loss function with a gradient penalty minimises the error between generated and ground truth images. Due to structure sparsity, the generated output may lose some information that leads to artifacts. Residual image learning with dilation and end‐to‐end training is used to resolve this issue by recovering high‐resolution anatomical details. Three different datasets: GoPro, Köhler, and Lai, with variant and in‐variant blur, are used to perform qualitative and quantitative analyses. Experiments show the proposed method is effective in reducing blur while preserving structural properties compared to multiple preprocessing techniques for image analysis. Moreover, the consistently improved performance over multiple publicly available datasets validates the merits of the proposed method for large data analysis.

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“…Each DRB block contains three 3 × 3 convolution layers with a batch normalisation layer. Consider an l th convolution layer output

x_{l}

as input of DRB and

r(\cdot )

be a residual mapping function, then each DRB block can be presented mathematically as [123],

\begin{eqnarray} R&=&r{\left(x_{l},W_{i}\right)}+x_{l}\nonumber\\ r{\left(x_{l},W_{i}\right)} &=& rW_{3}{\left[rW_{2}{\left(rW_{1}{\left(x_{l}\right)}\right)}\right]}\nonumber\\ D &=& F{\left({\left[x_{l},x_{l+1},x_{l+2},x_{l+3},R\right]}\right)}\nonumber\\ O&=&H{\left(D\right)} \end{eqnarray}

where O is the dense residual network block output, R and D are the outputs of residual and dense connections respectively,

([x_{l},x_{l+1},x_{l+2},x_{l+3},R])

represents the concatenation of feature maps in layers

(l,\ldots ,l+3)

and residual output

$R, r$<...>

…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Zhang et al. [123] utilise GAN to deblur remote sensing images. Nine residual blocks with stride convolution in the bottleneck section is used in the generative network.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Multi‐scale GAN with residual image learning for removing heterogeneous blur

Khan

Luo

2022

IET Image Processing

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“…2) Deep Learning Models: Deep learning has been extensively used in natural image deblurring [154] and demonstrated its advantage over handcrafted regularizers. Consequently, Zhang et al [155] proposed an end-to-end learnable method based on GANs for HSIs deblurring. However, the CNNbased models are only suitable for several specific types of blurs and have limits against more general spatially varying blurs.…”

Section: ) Sparsity Optimization Modelsmentioning

confidence: 99%

Image Restoration for Remote Sensing: Overview and Toolbox

Rasti¹,

Chen²,

Dalsasso³

et al. 2021

Preprint

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This paper is under review in GRSM. Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.

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“…A GAN-based methodology is also explored in [128] to deblur degraded remote sensing images in the context of image restoration. A common approach to solve this problem is the incorporation of various priors into a restoration procedure as constrained conditions that often lead to inaccurate results.…”

Section: Restorationmentioning

confidence: 99%

“…A common approach to solve this problem is the incorporation of various priors into a restoration procedure as constrained conditions that often lead to inaccurate results. In contrast, in [128] an end-to-end kernel-free blind deblurring learning method is presented that does not need any prior assumptions for the blurs. During training, a discriminator network is defined, with a gradient penalty which is shown to be robust to the choice of generator architecture, and a perceptual loss which is a simple ℓ2 loss based on the difference of the generated and target image CNN feature maps that focuses on restoring general content instead of texture details.…”

Section: Restorationmentioning

confidence: 99%

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Tsagkatakis

Aidini

Fotiadou

et al. 2019

Sensors

111

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Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.

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Generative Adversarial Network for Deblurring of Remote Sensing Image

Cited by 8 publications

References 10 publications

Multi‐scale GAN with residual image learning for removing heterogeneous blur

Multi‐scale GAN with residual image learning for removing heterogeneous blur

Image Restoration for Remote Sensing: Overview and Toolbox

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

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