Continuous digital zooming using local self-similarity-based super-resolution for an asymmetric dual camera system

Moon, Byeongho; Yu, Shuhui; Ko, Seungyong; Park, Seonhee; Paik, Joonki

doi:10.1364/josaa.34.000991

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Moon et al. [34] proposed a super‐resolution method using dual camera system. They fused wide‐ and tele‐view images by using the ideal high‐frequency component estimated from the optically zoomed image.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

“…They fused wide-and tele-view images by performing variational-based image restoration using the estimated PSF. Moon et al [34] proposed a super-resolution method using dual camera system. They fused wide-and teleview images by using the ideal high-frequency component estimated from the optically zoomed image.…”

Section: Application In Dual Camera Systemmentioning

confidence: 99%

Continuous digital zooming using generative adversarial networks for dual camera system

Yang

et al. 2021

IET Image Processing

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“…Obtaining satisfied high-resolution images by improving physical hardware is difficult and expensive. Therefore, the restoration technology that improves the image resolution through software has become a research hotspot [2].…”

Section: Introductionmentioning

confidence: 99%

Regularized super-resolution restoration algorithm for single medical image based on fuzzy similarity fusion

2019

J Image Video Proc.

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Medical images are blurred and noised due to various reasons in the acquirement, transmission and storage. In order to improve the restoration quality of medical images, a regular super-resolution restoration algorithm based on fuzzy similarity fusion is proposed. Based on maintained similarity in multiple scales, the fused similarity of the medical images is computed by fuzzy similarity fusion. First, fuzzy similarity is determined by the regional features. The images with certain similarity are obtained according to the maximum value, and the fused image is obtained by all obvious regional features. Then, an adaptive regularized restoration algorithm is employed. In order to ensure the objective function has a global optimal solution, regularized parameters of the global minimum solution of nonlinear function are solved iteratively. Finally, experimental results show that mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the restored image are visibly improved. The restored image also has an obvious improvement in the burr of local edge. Moreover, the algorithm has good stability with significantly enhanced PSNR.

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A binocular image fusion approach for minimizing false positives in handgun detection with deep learning

et al. 2019

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Continuous digital zooming using local self-similarity-based super-resolution for an asymmetric dual camera system

Cited by 4 publications

References 17 publications

Continuous digital zooming using generative adversarial networks for dual camera system

Continuous digital zooming using generative adversarial networks for dual camera system

Regularized super-resolution restoration algorithm for single medical image based on fuzzy similarity fusion

A binocular image fusion approach for minimizing false positives in handgun detection with deep learning

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