A Real-Time Super-Resolution Method Based on Convolutional Neural Networks

Fu, Shipeng; Lu, Lu; Hu, Li; Li, Zhen; Wu, Wei; Paul, Anand; Jeon, Gwanggil; Yang, Xiaobo

doi:10.1007/s00034-019-01283-y

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…Combined with the high-resolution finegrained 3D image fusion technology, the high-resolution fine-grained 3D image block reconstruction is achieved. 3 Reference 4 proposes a high-resolution finegrained 3D image reconstruction method based on block region contour detection. A high-resolution fine-grained 3D image mesoscale spatial information clustering model is established.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Fast 3D image reconstruction algorithm based on artificial intelligence technology

Pan

2021

International Journal of Electrical Engineering & Education

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In order to improve the 3D reconstruction capability of high-resolution fine-grained 3D images, a fast 3D image reconstruction algorithm based on artificial intelligence technology is proposed. The cross-gradient sharpening detection method is used to collect features and extract information from high-resolution fine-grained three-dimensional images, and establish an edge contour feature detection model for high-resolution fine-grained three-dimensional images. Combining the salient feature analysis method and the subspace feature analysis method to cluster and analyze the high-resolution fine-grained three-dimensional image. In the artificial intelligence environment, the saliency of the three-dimensional image is detected and analyzed, and the multi-dimensional segmentation and gray histogram of the high-resolution fine-grained three-dimensional image are reconstructed through the subspace segmentation method. According to the reconstruction results of the gray histogram, fast 3D image reconstruction and image fusion processing are performed. Finally, the accurate detection and recognition of the reconstructed image is realized. The simulation results show that this method has a good effect on 3D image reconstruction, and the time cost of image reconstruction is relatively short. It improves the recognition and feature analysis capabilities of high-resolution fine-grained 3D images, and has good application value in the reconstruction, detection and recognition of high-resolution fine-grained 3D images.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Fast 3D image reconstruction algorithm based on artificial intelligence technology

Pan

2021

International Journal of Electrical Engineering & Education

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“…Each face super-resolution method can be individually categorized by datasets, the size of images, and the ratio of scaling. We compare our method with four stat-of-the-art Fu et al [34]. These methods have the similar experimental conditions to ours to quantitatively and qualitatively compare our results.…”

Section: Comparison With State-of-the-artmentioning

confidence: 80%

“…However, our proposed method shows the best results in the pixeloriented criteria with 215 FPS. Fu et al [34] is one of the fastest real-time method for super-resolution, and our method is a slightly faster than it. The experimental results represents that our proposed method is able to show the competitive speed while maintaining high performance.…”

Section: B Computational Comparisonmentioning

confidence: 85%

Edge and Identity Preserving Network for Face Super-Resolution

Kim,

Li,

Yun

et al. 2020

Preprint

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Face super-resolution has become an indispensable part in security problems such as video surveillance and identification system, but the distortion in facial components is a main obstacle to overcoming the problems. To alleviate it, most stateof-the-arts have utilized facial priors by using deep networks. These methods require extra labels, longer training time, and larger computation memory. Thus, we propose a novel Edge and Identity Preserving Network for Face Super-Resolution Network, named as EIPNet, which minimizes the distortion by utilizing a lightweight edge block and identity information. Specifically, the edge block extracts perceptual edge information and concatenates it to original feature maps in multiple scales. This structure progressively provides edge information in reconstruction procedure to aggregate local and global structural information. Moreover, we define an identity loss function to preserve identification of super-resolved images. The identity loss function compares feature distributions between super-resolved images and target images to solve unlabeled classification problem. In addition, we propose a Luminance-Chrominance Error (LCE) to expand usage of image representation domain. The LCE method not only reduces the dependency of color information by dividing brightness and color components but also facilitates our network to reflect differences between Super-Resolution (SR) and High-Resolution (HR) images in multiple domains (RGB and YUV). The proposed methods facilitate our super-resolution network to elaborately restore facial components and generate enhanced 8× scaled super-resolution images with a lightweight network structure. Furthermore, our network can generate a 128 × 128 superresolved image with 215 fps on a GTX 1080Ti GPU. Extensive experimental results demonstrate that our network qualitatively and quantitatively outperforms state-of-the-art methods on two challenging datasets: CelebA and VGGFace2.

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