Continuous digital zooming of asymmetric dual camera images using registration and variational image restoration

Yu, Shuhui; Moon, Byeongho; Kim, Dong-Gyun; Kim, Sehoon; Choe, Wonhee; Lee, Sangkeun; Paik, Joonki

doi:10.1007/s11045-017-0534-4

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…Wilburn et al are one of the first to achieve higher imaging quality based on multiple camera arrays [47]. In the past, non-learningbased methods [48,49] focused on searching image similarity to achieve image registration, which tend to be low-efficiency and inaccurate. CameraSR tried to reverse the latent model which was regarded responsible for degradation of camera imagery due to intrinsic tradeoff between field of view and resolution [50].…”

Section: Dual Camera For Super Resolutionmentioning

confidence: 99%

AEFormer: Zoom Camera Enables Remote Sensing Super-Resolution via Aligned and Enhanced Attention

Tu,

Yang,

Tang

et al. 2023

Remote Sensing

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Reference-based super-resolution (RefSR) has achieved remarkable progress and shows promising potential applications in the field of remote sensing. However, previous studies heavily rely on existing and high-resolution reference image (Ref), which is hard to obtain in remote sensing practice. To address this issue, a novel structure based on a zoom camera structure (ZCS) together with a novel RefSR network, namely AEFormer, is proposed. The proposed ZCS provides a more accessible way to obtain valid Ref than traditional fixed-length camera imaging or external datasets. The physics-enabled network, AEFormer, is proposed to super-resolve low-resolution images (LR). With reasonably aligned and enhanced attention, AEFormer alleviates the misalignment problem, which is challenging yet common in RefSR tasks. Herein, it contributes to maximizing the utilization of spatial information across the whole image and better fusion between Ref and LR. Extensive experimental results on benchmark dataset RRSSRD and real-world prototype data both verify the effectiveness of the proposed method. Hopefully, ZCS and AEFormer can enlighten a new model for future remote sensing imagery super-resolution.

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Section: Dual Camera For Super Resolutionmentioning

confidence: 99%

AEFormer: Zoom Camera Enables Remote Sensing Super-Resolution via Aligned and Enhanced Attention

Tu,

Yang,

Tang

et al. 2023

Remote Sensing

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“…They simulated tele-image by center-cropping HR and performing random affine transformation, and formulate this task to image registration. Yu et al [37] applied RANSAC algorithm [10] on SURF [4] features to conduct global registration. Manne et al [20] applied FLANN algorithm [24] on ORB features [26] for geometric registration.…”

Section: Dual Camera Super Resolutionmentioning

confidence: 99%

Dual-Camera Super-Resolution with Aligned Attention Modules

Wang

Xie²,

Sun³

et al. 2021

Preprint

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We present a novel approach to reference-based superresolution (RefSR) with the focus on dual-camera superresolution (DCSR), which utilizes reference images for high-quality and high-fidelity results. Our proposed method generalizes the standard patch-based feature matching with spatial alignment operations. We further explore the dualcamera super-resolution that is one promising application of RefSR, and build a dataset that consists of 146 image pairs from the main and telephoto cameras in a smartphone. To bridge the domain gaps between realworld images and the training images, we propose a selfsupervised domain adaptation strategy for real-world images. Extensive experiments on our dataset and a public benchmark demonstrate clear improvement achieved by our method over state of the art in both quantitative evaluation and visual comparisons. Our code and data are available at https://tengfei-wang.github.io/ Dual-Camera-SR/index.html.

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“…In 2017, Yu et al. [33] presented a theoretical basis to realize a high‐quality digital zooming using two camera modules with different focal lengths. They fused wide‐ and tele‐view images by performing variational‐based image restoration using the estimated PSF.…”

Section: Related Workmentioning

confidence: 99%

Continuous digital zooming using generative adversarial networks for dual camera system

Yang

et al. 2021

IET Image Processing

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This paper presents a generative adversarial network (GAN) with patch match algorithm to realize a high-quality digital zooming using two camera modules with different focal lengths. In dual camera system, shorter focal length module produces the wide-view image with the low resolution. On the other hand, the longer focal length module produces the tele-view image via optical zooming. The long-focal image contains more details than short-focal image and can be used to guide short-focal image to reconstruct high frequency part. Firstly, a feature extraction block (FEB) is advanced to extract feature of long-focal image and short focal-image to reconstruct a wide-view image with different resolutions. Next, a patch match algorithm is integrated into convolution neural networks (CNN) to fuse information of long-focal with short-focal image and generate a new fused image. Finally, the fused image and short-focal image are merged with a feature fusion block (FFB) to predict high-resolution images. In addition, generative adversarial networks are used for filtering information integrated by previous network and output the zoomed image. Extensive experiments on benchmark datasets show that our algorithm achieves favorable performance against state-of-the-art methods.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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Continuous digital zooming of asymmetric dual camera images using registration and variational image restoration

Cited by 5 publications

References 33 publications

AEFormer: Zoom Camera Enables Remote Sensing Super-Resolution via Aligned and Enhanced Attention

AEFormer: Zoom Camera Enables Remote Sensing Super-Resolution via Aligned and Enhanced Attention

Dual-Camera Super-Resolution with Aligned Attention Modules

Continuous digital zooming using generative adversarial networks for dual camera system

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