A Progressively Enhanced Network for Video Satellite Imagery Superresolution

Jiang, Kui; Wang, Zhongyuan; Yi, Peng; Jiang, Junjun

doi:10.1109/lsp.2018.2870536

Cited by 62 publications

(12 citation statements)

References 13 publications

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“…Validation for an upscale factor of 3 on a diverse set of scenes acquired by the Jilin-1 platform demonstrates an increase between 0.5 and 1.7 dB compared to SRCNN. Jiang et al [93] consider a two-stage process for video frame super-resolution, where a pre-trained CNN with an architecture similar to [16] produced multiple features at the first stage, which are then combined though a densely connected network at the second stage. The proposed PECNN architecture achieve a minor increase in terms of no-reference quality metrics, such as the average gradient of the naturalness image quality when evaluated on aerial imagery from the Kaggle dataset; however, the method demonstrates a substantial reduction in terms of processing time per image.…”

Section: Super-resolutionmentioning

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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Section: Super-resolutionmentioning

confidence: 99%

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Tsagkatakis

Aidini

Fotiadou

et al. 2019

Sensors

111

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show abstract

“…Image super-resolution plays a vital role in the field of image and computer visionbased applications because the high quality or high-resolution (HR) images have more pixel density level and contains more detailed information. The detailed information is applied in various fields of computer vision and image processing tasks, such as image restoration [1], security surveillance [2], closed-circuit television surveillance [3], and security systems [4], object recognition [5], object detection [6], satellite imaging [7], remote sensing imagery [8][9][10], autonomous driverless car [11], medical imaging [12][13][14], and atmospheric monitoring [15].…”

Section: Introductionmentioning

confidence: 99%

Multi-Path Deep CNN with Residual Inception Network for Single Image Super-Resolution

et al. 2021

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Recent research on single-image super-resolution (SISR) using deep convolutional neural networks has made a breakthrough and achieved tremendous performance. Despite their significant progress, numerous convolutional neural networks (CNN) are limited in practical applications, owing to the requirement of the heavy computational cost of the model. This paper proposes a multi-path network for SISR, known as multi-path deep CNN with residual inception network for single image super-resolution. In detail, a residual/ResNet block with an Inception block supports the main framework of the entire network architecture. In addition, remove the batch normalization layer from the residual network (ResNet) block and max-pooling layer from the Inception block to further reduce the number of parameters to preventing the over-fitting problem during the training. Moreover, a conventional rectified linear unit (ReLU) is replaced with Leaky ReLU activation function to speed up the training process. Specifically, we propose a novel two upscale module, which adopts three paths to upscale the features by jointly using deconvolution and upsampling layers, instead of using single deconvolution layer or upsampling layer alone. The extensive experimental results on image super-resolution (SR) using five publicly available test datasets, which show that the proposed model not only attains the higher score of peak signal-to-noise ratio/structural similarity index matrix (PSNR/SSIM) but also enables faster and more efficient calculations against the existing image SR methods. For instance, we improved our method in terms of overall PSNR on the SET5 dataset with challenging upscale factor 8× as 1.88 dB over the baseline bicubic method and reduced computational cost in terms of number of parameters 62% by deeply-recursive convolutional neural network (DRCN) method.

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“…In this paper, we focus on resolving video super-resolution (VSR) via a deep learning method. VSR [1,2] refers to the reconstruction of high-resolution (HR) video sequences from their low resolution (LR) counterparts, which is widely used in high-definition television (HDTV), human face hallucination [3][4][5], remote sensing [6][7][8] etc. Compared with single image super-resolution (SISR), VSR was provided with huge temporal redundancies existing in consecutive frames, which is crucial for the success of SR reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Video super‐resolution with non‐local alignment network

Zhou

Chen

Ding

et al. 2021

IET Image Processing

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Video super-resolution (VSR) aims at recovering high-resolution frames from their lowresolution counterparts. Over the past few years, deep neural networks have dominated the video super-resolution task because of its strong non-linear representational ability. To exploit temporal correlations, most deep neural networks have to face two challenges: (1) how to align consecutive frames containing motions, occlusions and blurring, and establish accurate temporal correspondences, (2) how to effectively fuse aligned frames and balance their contributions. In this work, a novel video super-resolution network, named NLVSR, is proposed to solve above problems in an efficient and effective manner. For alignment, a temporal-spatial non-local operation is employed to align each frame to the reference frame. Compared with existing alignment approaches, the proposed temporalspatial non-local operation is able to integrate the global information of each frame by a weighted sum, leading to a better performance in alignment. For fusion, an attentionbased progressive fusion framework was designed to integrate aligned frames gradually. To penalize the points with low-quality in aligned features, an attention mechanism was employed for a robust reconstruction. Experimental results demonstrate the superiority of the proposed network in terms of quantitative and qualitative evaluation, and surpasses other state-of-the-art methods by 0.33 dB at least. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A Progressively Enhanced Network for Video Satellite Imagery Superresolution

Cited by 62 publications

References 13 publications

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Multi-Path Deep CNN with Residual Inception Network for Single Image Super-Resolution

Video super‐resolution with non‐local alignment network

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