Face image super-resolution via sparse representation and wavelet transform

Fanaee, Farnaz; Yazdi, Mehran; Faghihi, Mohammad Amin

doi:10.1007/s11760-018-1330-9

Cited by 15 publications

(4 citation statements)

References 10 publications

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“…In recent years, in order to take advantage of the sparsity and multiresolution of wavelet transform [29], a surge of approaches [30][31][32][33][34][35] with the wavelet technology have been proposed on image super resolution. Among these algorithms, [30][31][32][33] adopt the combination of the discrete wavelet transform and sparse representation instead of deep learning to obtain the HR image. Guo et al [34] proposed DWSR as the first approach to predict high-resolution images in wavelet domain with a deep CNN network.…”

Section: Related Work 21 Wavelet-based Image Super Resolutionmentioning

confidence: 99%

Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution

She

Liu

2021

Micromachines

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Medical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep learning approaches has recently demonstrated high-quality reconstruction for medical image super-resolution. In this work, we propose a light-weight wavelet frequency separation attention network for medical image super-resolution (WFSAN). WFSAN is designed with separated-path for wavelet sub-bands to predict the wavelet coefficients, considering that image data characteristics are different in the wavelet domain and spatial domain. In addition, different activation functions are selected to fit the coefficients. Inputs comprise approximate sub-bands and detail sub-bands of low-resolution wavelet coefficients. In the separated-path network, detail sub-bands, which have more sparsity, are trained to enhance high frequency information. An attention extension ghost block is designed to generate the features more efficiently. All results obtained from fusing layers are contracted to reconstruct the approximate and detail wavelet coefficients of the high-resolution image. In the end, the super-resolution results are generated by inverse wavelet transform. Experimental results show that WFSAN has competitive performance against state-of-the-art lightweight medical imaging methods in terms of quality and quantitative metrics.

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Section: Related Work 21 Wavelet-based Image Super Resolutionmentioning

confidence: 99%

Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution

She

Liu

2021

Micromachines

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“…Sparse representation theory is widely used in face recognition [20], image denoising [21], target tracking and other fields because of its excellent data feature representation ability. Ma [22] established the target observation model with BOMP as the core, introduced the sparse display into the particle filtering framework, and found the optimal solution after optimization.…”

Section: Introductionmentioning

confidence: 99%

Real-time textile fabric flaw inspection system using grouped sparse dictionary

Wang

Yan

Pan

et al. 2023

J Real-Time Image Proc

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Fabric surface flaw inspection is essential for textile quality control, and it is demanding to replace human inspectors with the automatic machine vision-based flaw inspection system. To alleviate the time-consuming problem of sparse coding in detecting phase, this work presents a real time fabric flaw inspection method by using grouped sparse dictionary. Firstly, the over-complete sparse dictionary is learned from normal fabric images; Secondly, the learned sparse dictionary is grouped into several subdictionaries by evaluating reconstruction error. Finally, the grouped dictionary is used to represent image and identify flaw regions as they cannot be represented well, leading to large reconstruction error. In addition, a non-maximum suppression algorithm is also proposed to reduce false inspection further. Experiments on various fabric flaws and real-time implementation on the proposed vision-based hardware system are conducted to evaluate the performance of proposed method. In comparison with other dictionary learning methods, the experimental results demonstrate that the proposed method can reduce the running time significantly and achieve a decent performance, which is capable of meeting the real-time inspection requirement without compromising inspection accuracy.

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“…SISR establishes a mapping between LR image and HR image and uses the mapping to map a single LR image to the desired HR image. Traditional machine learning algorithms have been developed to learn the mapping, such as methods based on self-example learning [10], neighborhood embedding [11], and sparse representation [12]. Currently, the mainstream of SISR research is the methods based on deep learning.…”

Section: Introductionmentioning

confidence: 99%

A Robust Multiframe Image Super-Resolution Method in Variational Bayesian Framework

Min

Fan

2022

Mathematical Problems in Engineering

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Multiframe image super-resolution (MISR) combines complementary information of a set of low-resolution (LR) images to reconstruct a high-resolution (HR) one. In this study, we propose a robust and fully data-driven MISR method in the variational Bayesian framework. Different from the existing variational super-resolution (SR) methods, we use the l1 norm-based observation model, which takes the acquisition noise, outliers, and impulse noise into account. Furthermore, we have evaluated three typical image prior models, and the most appropriate one is chosen for our proposed method. The proposed method has the following advantages: (1) the HR image and all parameters are automatically estimated in an optimal stochastic sense; (2) the algorithm is robust to impulse noise and outliers. Extensive experiments with synthetic and real images demonstrate the advantages of the proposed method.

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Face image super-resolution via sparse representation and wavelet transform

Cited by 15 publications

References 10 publications

Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution

Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution

Real-time textile fabric flaw inspection system using grouped sparse dictionary

A Robust Multiframe Image Super-Resolution Method in Variational Bayesian Framework

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