Weighted Adaptive Image Super-Resolution Scheme Based on Local Fractal Feature and Image Roughness

Yao, Xunxiang; Wu, Qiang; Zhang, Peng; Bao, Fangxun

doi:10.1109/tmm.2020.2997126

Cited by 13 publications

(8 citation statements)

References 54 publications

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“…Over recent years, deep learning has enabled significant progress to be made in the fields of image classification [25][26][27][28] and feature learning [29][30][31][32]. One of the key drivers of the success of deep learning is the availability of large amounts of annotated data.…”

Section: Datasetmentioning

confidence: 99%

Automatic Classification of Pollen Grain Microscope Images Using a Multi-Scale Classifier with SRGAN Deblurring

Chen

2022

Applied Sciences

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Pollen allergies are seasonal epidemic diseases that are accompanied by high incidence rates, especially in Beijing, China. With the development of deep learning, key progress has been made in the task of automatic pollen grain classification, which could replace the time-consuming and laborious manual identification process using a microscope. In China, few pioneering works have made significant progress in automatic pollen grain classification. Therefore, we first constructed a multi-class and large-scale pollen grain dataset for the Beijing area in preparation for the task of pollen classification. Then, a deblurring pipeline was designed to enhance the quality of the pollen grain images selectively. Moreover, as pollen grains vary greatly in size and shape, we proposed an easy-to-implement and efficient multi-scale deep learning architecture. Our experimental results showed that our architecture achieved a 97.7% accuracy, based on the Resnet-50 backbone network, which proved that the proposed method could be applied successfully to the automatic identification of pollen grains in Beijing.

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Section: Datasetmentioning

confidence: 99%

Automatic Classification of Pollen Grain Microscope Images Using a Multi-Scale Classifier with SRGAN Deblurring

Chen

2022

Applied Sciences

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“…Fractal theory can condense the numerous and complex texture features of image into a concise digital expression (Acquisgrana et al, 2022), that is, fractal dimension, which can reveal the hidden information of jujube quality change. The theory was proposed by Professor Mandelbrot (Mandelbrot, 1967), and is often used to process vibration signals (Huang et al, 2022) and texture features (Yao et al, 2021). Its application fields include fault diagnosis (Liang et al, 2022), tool wear detection (Zhao et al, 2021), face recognition (Tang et al, 2018), medical diagnosis (Ibrahim et al, 2022), etc.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of maturity and moisture content in fresh jujube using fractal theory and tissue images

ZHANG

Luo

et al. 2023

Food Sci. Technol

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This study proposes a novel method for jujube maturity and water content determination incorporating fractal theory. Firstly, the tissue section images of winter jujube at different maturity and tissue section image images of winter jujube with different moisture content were preprocessed by grayscale, median filter, histogram equalization and binarization. Secondly, the fractal dimension of jujube tissue section images of jujube at different maturity, and the fractal dimension of tissue slice images of jujube with different moisture content were calculated based on the box dimension algorithm. Finally, the relationships of the fractal dimension-maturity and fractal dimension-moisture content were explored. The results showed that the fractal features had good discrimination performance, the fractal dimension decreased with the increase of maturity, and the fractal dimension decreased with the increase of moisture content. This study provides a new way of thinking about the detection of physical and chemical indicators of winter jujube and other fruits.

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“…For instance, image zooming algorithms based on surface fitting technology [4,5], and gradient direction-oriented image zooming algorithms [6,7], typically produce distortion around the edges and textured regions; image zooming methods based on the wavelet transform [8,9] are prone to the ring phenomenon; nonlocal iterative back-projection algorithm based on a sparse representation [10] and image zooming method combining interpolation and similarity [11] produce transition smoothing in the nonmarginal pixels of the zoomed image, and the visual quality of the zoomed image is reduced. Some scholars have studied the image zooming method based on local fractal feature [12]. For some images with obvious nonfractal features, the experimental results are not satisfactory.…”

Section: Introductionmentioning

confidence: 99%

An image zooming method based on the coupling threshold in the wavelet packet transform domain

Liu,

Wang

2023

Meas. Sci. Technol.

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Aiming at the problem of blurring edge and detail information in the process of image zooming, this paper proposes a new method of image zooming based on wavelet packet transform by combining the characteristics of anisotropic diffusion. First, the initial zoomed image with higher resolution is obtained by wavelet transform, and the wavelet packet decomposition is performed to obtain more high-frequency wavelet packet coefficients reflecting image details. Second, due to the existence of noise, the relationship between wavelet packet transform and anisotropic diffusion is obtained by studying the process of wavelet packet threshold denoising, and the expression of coupling threshold based on diffusion function is given and applied to the high-frequency wavelet packet coefficients. Finally, the original image is used as the low-frequency part after passing the soft threshold, and reconstructed with the denoised high-frequency part to obtain the final zoomed image. The traditional zooming algorithms as well as the learning-based zooming algorithms are selected for comparison. The results show that the algorithm in this paper effectively avoids the blurring of edges and details under the premise of ensuring the similarity between the zoomed image and the original image, so that the zoomed image can obtain more high-frequency information, and achieves the purpose of removing the noise as well as enhancing the detailed information of the image. The effectiveness of this paper’s algorithm in edge protection can be seen from the results of the comparison with the deep learning-based zooming algorithms.

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Weighted Adaptive Image Super-Resolution Scheme Based on Local Fractal Feature and Image Roughness

Cited by 13 publications

References 54 publications

Automatic Classification of Pollen Grain Microscope Images Using a Multi-Scale Classifier with SRGAN Deblurring

Automatic Classification of Pollen Grain Microscope Images Using a Multi-Scale Classifier with SRGAN Deblurring

Evaluation of maturity and moisture content in fresh jujube using fractal theory and tissue images

An image zooming method based on the coupling threshold in the wavelet packet transform domain

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