Unsupervised Remoting Sensing Super-Resolution via Migration Image Prior

Wang, Jiaming; Shao, Zhenfeng; Lü, Tao; Huang, Xiao; Zhang, Ruiqian; Wang, Yu

doi:10.1109/icme51207.2021.9428093

Cited by 9 publications

(5 citation statements)

References 24 publications

(36 reference statements)

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“…Migrating features of reference samples precisely is crucial to the SR reconstruction of the target image 32 . In this paper, we first extract the feature maps of the reference samples and the target image, and then migrate features of reference samples through similarity comparison.…”

Section: Methodsmentioning

confidence: 99%

“…Migrating features of reference samples precisely is crucial to the SR reconstruction of the target image. 32 In this paper, we first extract the feature maps of the reference samples and the target image, and then migrate features of reference samples through similarity comparison. The LR image is Â2 upsampled and the multiple reference images (MultiRef) are first downsampled and then upsampled with bicubic interpolations to ensure the domain consistency; the results are denoted as LR" and MultiRef#" respectively.…”

Section: Feature Migration Of Multiple Referencesmentioning

confidence: 99%

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Semi‐supervised super‐resolution of diffusion‐weighted images based on multiple references

Guo

Wang

et al. 2023

NMR in Biomedicine

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Spatial resolution of diffusion tensor images is usually compromised to accelerate the acquisitions, and the state-of-the-art (SOTA) image super-resolution (SR) reconstruction methods are commonly based on supervised learning models.Considering that matched low-resolution (LR) and high-resolution (HR) diffusionweighted (DW) image pairs are not readily available, we propose a semi-supervised DW image SR reconstruction method based on multiple references (MRSR) extracted from other subjects. In MRSR, the prior information of multiple HR reference images is migrated into a residual-like network to assist SR reconstruction of DW images, and a CycleGAN-based semi-supervised strategy is used to train the network with 30% matched and 70% unmatched LR-HR image pairs. We evaluate the performance of the MRSR by comparing against SOTA methods on an HCP dataset in terms of the quality of reconstructed DW images and diffusion metrics. MRSR achieves the best performance, with the mean PSNR/SSIM of DW images being improved by at least 14.3%/28.8% and 1%/1.4% respectively relative to SOTA unsupervised and supervised learning methods, and with the fiber orientations deviating from the ground truth by about 6.28 on average, the RMSEs of fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity being 3.0%, 4.6%, 5.7% and 4.5% respectively relative to the ground truth. We validate the effectiveness of the proposed network structure, multiple-reference and CycleGAN-based semi-supervised learning strategies for SR reconstruction of diffusion tensor images through the ablation studies. The proposed method allows us to achieve SR reconstruction for diffusion tensor images with a limited number of matched image pairs.

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Section: Methodsmentioning

confidence: 99%

Section: Feature Migration Of Multiple Referencesmentioning

confidence: 99%

Semi‐supervised super‐resolution of diffusion‐weighted images based on multiple references

Guo

Wang

et al. 2023

NMR in Biomedicine

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“…Based on zero-shot learning, author proposed DualSR [23] for real-world SR by employing the adversarial loss and GAN. Another work, MIP [24] designed GAN for image reconstruction. Nevertheless, GAN-based image SR methods introduce some artifacts that do not exist in the original image and are not helpful for remote sensing applications.…”

Section: B Few-shot and Zero-shot Learningmentioning

confidence: 99%

Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution

Mishra

Hadar

2023

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

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Real-world degradations deviate from ideal degradations, as most deep learning-based scenarios involve the ideal synthesis of low-resolution (LR) counterpart images by popularly used bicubic interpolation. Moreover, supervised learning approaches rely on many high-resolution (HR) and LR image pairings to reconstruct missing information based on their association, developed by complex long hours of deep neural network training. Additionally, the trained model's generalizability on various image datasets with various distributions is not guaranteed. To overcome this challenge, we proposed our novel Self-FuseNet, particularly for extremely poor-resolution satellite images. Also, the network exhibits strong generalization performance on additional datasets (both "ideal" and "nonideal" scenarios). The network is especially for those image datasets suffering from the following two significant limitations: 1) nonavailability of ground truth HR images; 2) limitation of a large count of the unpaired dataset for deep neural network training. The benefit of the proposed model is threefold: 1) it does not require any significant extensive training data, either paired or unpaired but only a single LR image without prior knowledge of its distribution; 2) it is a simple and effective model for super-resolving very poor-resolution images, saving computational resources and time; 3) using UNet, the processing of data are accelerated by the network's wide skip connections, allowing image reconstruction with fewer parameters. Rather than using an inverse approach, as common in most deep learning scenarios, we introduced a forward approach to super-resolve exceptionally LR remote sensing images. This demonstrates its supremacy over recently proposed state-of-the-art methods for unsupervised single real-world image blind super-resolution.

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“…GANs consist of two sub-models: the generator model to train to generate new examples and the discriminator model that tries to classify examples as either real (from the domain) or fake (generated). GANs find their use across a range of problem domains, notably in image-image translation [11]- [15].…”

Section: Introductionmentioning

confidence: 99%

“…A method was proposed for single image super-resolution [11], providing a direct end-to-end mapping between both the low-and high-resolution images. This method makes use of a deep convolutional neural network that takes the low-resolution image as input and outputs the high-resolution one.…”

Section: Introductionmentioning

confidence: 99%

Development of image super-resolution framework

Shekar¹,

Deepthi²,

Rahul³

et al. 2023

IJRA

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There are some scenarios where the images taken are of low resolution and it is hard to judge the features from them, resulting in the need for enhancement. Super-resolution is a technique to produce a high-resolution image from a lower-resolution image. The intention here is to develop a system that enhances images of faces and satellite images by integrating these models and providing an interface to access this model. There have been various ways of achieving super-resolution using different techniques. Throughout the years, techniques involving deep learning methods, interpolation techniques, and recursive networks have been explored. We find it promising to use generative adversarial networks (GANs). The system has been deployed through Google Collaborate, Python libraries, and the TensorFlow framework. To assess the developed system, which consists of images, three metrics have been calculated. namely, peak signal-to-noise ratio, mean squared error, and structural similarity index. The model successfully demonstrated the capability of GANs by efficiently generating a high-resolution image from a low-resolution image for the given cases. The model would then be run on a standalone server for free Internet access for users to use super-resolution facial images and satellite images.

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Unsupervised Remoting Sensing Super-Resolution via Migration Image Prior

Cited by 9 publications

References 24 publications

Semi‐supervised super‐resolution of diffusion‐weighted images based on multiple references

Semi‐supervised super‐resolution of diffusion‐weighted images based on multiple references

Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution

Development of image super-resolution framework

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