Sparse Representation Based Image Super‐resolution Using Large Patches

Liu, Ning; Zhou, Pan; Liu, Wenju; Deng, Ke

doi:10.1049/cje.2018.05.011

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…The LBP operator calculates the gray value difference between the center of the region and the surrounding pixels. The binary weights are calculated according to the positive and negative, and the binary pattern of the domain is calculated to describe the change of the image texture [17]. The representation of the local area of the image can be described by the average distribution of gray values, as shown in Formula (9): ) ,..., , (…”

Section: Figure 5 Haar-like Featuresmentioning

confidence: 99%

Blended Teaching Evaluation Index System Based on AI Emotion Recognition

2023

IJEIS

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With the continuous development of information technology, the teaching method is not limited to offline teaching. Online teaching is on the rise, and more and more teachers use a blended teaching evaluation system that combines online and offline to carry out classroom teaching. However, during online teaching, teachers cannot remind students when students are not paying attention, resulting in low classroom quality. This paper applied artificial intelligence (AI) emotion recognition technology to the blended teaching evaluation index system. When students are studying in the course, the emotion recognition is carried out on the students; when the students are not concentrating, the students are reminded to ensure the quality of the students' learning. Through testing different classes it was found that: the application of AI emotion recognition technology to the blended teaching evaluation system can improve the quality of classrooms, and improve students' learning status and student performance. Student satisfaction had increased by 9.7%, and AI emotion recognition technology can optimize the evaluation index system of blended teaching.

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Section: Figure 5 Haar-like Featuresmentioning

confidence: 99%

Blended Teaching Evaluation Index System Based on AI Emotion Recognition

2023

IJEIS

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“…In the past few years, many CNN-based RGB image SR methods have been proposed. In [12], the CNN method was first applied to RGB image SR, and then many innovative CNN architectures were proposed to improve SR performance [13]. Inspired by this, more and more methods employ the CNN framework to conduct HSI SR.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution Based on Spatial-Spectral Feature Extraction Network

Chen

Luo

et al. 2023

Chinese J. Elect.

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Constrained by the physics of hyperspectral sensors, the spatial resolution of hyperspectral images (HSI) is low. Hyperspectral image super-resolution (HSI SR) is a task to obtain high-resolution hyperspectral images from low-resolution hyperspectral images. Existing algorithms have the problem of losing important spectral information while improving spatial resolution. To handle this problem, a spatial-spectral feature extraction network (SSFEN) for HSI SR is proposed in this paper. It enhances the spatial resolution of the HSI while preserving the spectral information. The SSFEN is composed of three parts: spatial-spectral mapping network, spatial reconstruction network, and spatial-spectral fusing network. And a joint loss function with spatial and spectral constraints is designed to guide the training of the SSFEN. Experiment results show that the proposed method improves the spatial resolution of the HSI and effectively preserves the spectral information simultaneously.

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Sparse Representation Based Image Super‐resolution Using Large Patches

Cited by 2 publications

References 26 publications

Blended Teaching Evaluation Index System Based on AI Emotion Recognition

Blended Teaching Evaluation Index System Based on AI Emotion Recognition

Hyperspectral Image Super-Resolution Based on Spatial-Spectral Feature Extraction Network

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