Journal ArticleDOI
Hyperspectral Image Superresolution Using Spectrum and Feature Context
Qi Wang,Qiang Li,Xuelong Li +2 more
TLDR
This work designs a dual-channel network through 2D and 3D convolution to jointly exploit the information from both single band and adjacent bands, which is different from previous works.Abstract:
Deep learning-based hyperspectral image superresolution methods have achieved great success recently. However, most methods utilize 2D or 3D convolution to explore features, and rarely combine the two types of convolution to design networks. Moreover, when the model only contains 3D convolution, almost all the methods take all the bands of hyperspectral image as input to analyze, which requires more memory footprint. To address these issues, we explore a new structure for hyperspectral image superresolution using spectrum and feature context. Inspired by the high similarity among adjacent bands, we design a dual-channel network through 2D and 3D convolution to jointly exploit the information from both single band and adjacent bands, which is different from previous works. Under the connection of depth split, it can effectively share spatial information so as to improve the learning ability of 2D spatial domain. Besides, our method introduces the features extracted from previous band, which contributes to the complementarity of information and simplifies the network structure. Through feature context fusion, it significantly enhances the performance of the algorithm. Extensive evaluations and comparisons on three public datasets demonstrate that our approach produces the state-of-the-art results over the existing approaches.read more
Citations
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Journal ArticleDOI
Exploring the Relationship Between 2D/3D Convolution for Hyperspectral Image Super-Resolution
Qiang Li,Qi Wang,Xuelong Li +2 more
TL;DR: This paper proposes a novel hyperspectral image SR method that alternately employs 2D and 3D units to solve the problem of structural redundancy by sharing spatial information during reconstruction for existing model, which can enhance the learning ability of 2D spatial domain.
Journal ArticleDOI
Interactformer: Interactive Transformer and CNN for Hyperspectral Image Super-Resolution
TL;DR: In this paper , a separable self-attention module with linear complexity is designed to solve the problem that traditional self-Attention mechanisms suffer from large memory costs due to quadratic complexity.
Journal ArticleDOI
Deep Unfolding Network for Spatiospectral Image Super-Resolution
TL;DR: Jiang et al. as mentioned in this paper proposed an unfolding spatio-spectral super-resolution network (US3RN), which not only uses closed-form solutions to solve SISR subproblem and SSR sub-problem, but also has extremely small parameters (only 295 K).
Journal ArticleDOI
NonRegSRNet: A Nonrigid Registration Hyperspectral Super-Resolution Network
TL;DR: In this article , the authors proposed a novel unsupervised spectral unmixing and image deformation correction network with multimodal and multitask learning that can be used for the joint registration of HSI and MSI and to produce SR imagery.
Journal ArticleDOI
Cascaded Convolutional Neural Network-Based Hyperspectral Image Resolution Enhancement via an Auxiliary Panchromatic Image
TL;DR: Wang et al. as mentioned in this paper proposed a two-stage cascaded CNN to reconstruct the anticipated high-resolution hyperspectral (HS) image, which can improve the spatial resolution and spectral fidelity of HS image, and achieve better performance than some state-of-theart HS pan-sharpening algorithms.
References
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Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Proceedings ArticleDOI
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig,Lucas Theis,Ferenc Huszar,Jose Caballero,Andrew Cunningham,Alejandro Acosta,Andrew Peter Aitken,Alykhan Tejani,Johannes Totz,Zehan Wang,Wenzhe Shi +10 more
TL;DR: SRGAN as mentioned in this paper proposes a perceptual loss function which consists of an adversarial loss and a content loss, which pushes the solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.
Journal ArticleDOI
Image Super-Resolution Using Deep Convolutional Networks
TL;DR: Zhang et al. as discussed by the authors proposed a deep learning method for single image super-resolution (SR), which directly learns an end-to-end mapping between the low/high-resolution images.
Posted Content
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig,Lucas Theis,Ferenc Huszar,Jose Caballero,Andrew Cunningham,Alejandro Acosta,Andrew Peter Aitken,Alykhan Tejani,Johannes Totz,Zehan Wang,Wenzhe Shi +10 more
TL;DR: SRGAN, a generative adversarial network (GAN) for image super-resolution (SR), is presented, to its knowledge, the first framework capable of inferring photo-realistic natural images for 4x upscaling factors and a perceptual loss function which consists of an adversarial loss and a content loss.
Proceedings ArticleDOI
Enhanced Deep Residual Networks for Single Image Super-Resolution
TL;DR: This paper develops an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods, and proposes a new multi-scale deepsuper-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model.
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