Nonblind Image Deblurring via Deep Learning in Complex Field

Quan, Yuhui; Lin, Peikang; Xu, Yong; Nan, Yuesong; Gao, Hui

doi:10.1109/tnnls.2021.3070596

Cited by 23 publications

(10 citation statements)

References 47 publications

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“…But it has not been applied to the blur of natural captured images. QuanY [21] introduced a CNN-based image prior defined in the Gabor domain for the noise problem of deblurring tasks, which provides a better performance for noise suppression. Whereas this algorithm is only suitable for the case of known blur kernels, which is somewhat powerless for blind deblurring.…”

Section: A Motion Deblurringmentioning

confidence: 99%

“…While the learning rate of the generator is set to after 50 epochs, with the initial parameter selected as . [17] and ChoS et al(2021) [21]. It is to be noted that the attention-adaptive and deformable convolution modules (AAM, DCM) are proposed in ChenL's method, which are considered to be used in methods such as DebluurGAN, MSCNN and SRN, etc in order to verify the effect of these two modules.…”

Section: B Implementation Detailsmentioning

confidence: 99%

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High-Frequency Attention Residual GAN Network for Blind Motion Deblurring

et al. 2022

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The moving image deblurring method based on deep learning has achieved good results. However, some methods are not effective in restoring image texture detail information. Therefore, this paper proposes a High-Frequency Attention Residual Module (HFAR), which is used to guide the network to learn more high-frequency texture information in the image to improve the quality of image detail restoration. The designed attention residual module consists of two sub-modules, Fourier Channel Attention module (FCA) and Edge Spatial Attention module (ESA). The FCA module gives more weight to the feature maps that contain more high-frequency information in multiple channels. While the ESA module gives more weight to the areas in the feature maps which contain more high-frequency information to guide the network to learn image details and texture information. Extensive experiments on different datasets show that our method achieves state-of-the-art performance in motion deblurring.

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Section: A Motion Deblurringmentioning

confidence: 99%

Section: B Implementation Detailsmentioning

confidence: 99%

High-Frequency Attention Residual GAN Network for Blind Motion Deblurring

et al. 2022

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“…e literature [18] mentions, respectively, the use of TV regularization and l p parametric regularization methods to fit the heavy-tailed distribution of image gradients. In addition to TV regularization and l p parametric regularization methods, the concept of learning-based image restoration has been proposed by some scholars in recent years.…”

Section: Selection Of Canonical Terms and Expert Functions Under The ...mentioning

confidence: 99%

“…Experiments. Using accuracy and time-consuming experiments as indicators, literature [5], literature [18], and literature [20] were used as control groups for the experiments and their experimental results were compared with the experimental results of this research method.…”

Section: Comparison Of Quantitativementioning

confidence: 99%

Image Deblurring Algorithm Based on the Gaussian-Scale Mixture Expert Field Model

Zhang

Zhang²

2022

Mathematical Problems in Engineering

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With the proliferation of portable digital products, image quality degradation has received a lot of attention. As the most common phenomenon in image degradation, the issue of image deblurring is the focus of much attention. Blind motion blur removal is the main target of this paper. The heavy-tailed distribution is the most dominant statistical feature of natural images. However, most image deblurring methods use a gradient prior with fixed parameters to recover a clear image, which leads to loss of details in the recovered clear image and does not consider the higher order prior of the natural image. Therefore, this paper proposes a new regularized image recovery model based on the Gaussian-scale mixture expert field(GSM-FOE) model. First, the GSM-FoE model learns filters and corresponding parameters with higher order prior information of images by training images in a natural image library; second, these learning results are used to guide the image recovery process. The GSM-FoE model and gradient-fidelity based image recovery model is proposed, which can be used with an iterative re-weighted least squares (IRLS) method. Experiments demonstrate that the suggested recovery approach is simple to use and successful at reducing blur and noise, as well as suppressing ringing effects while preserving image information. Moreover, the image restoration method performs well for large blurring kernels. The results fully reflect the effectiveness and robustness of the proposed method for complex noise scenarios. The quality of the generated images is significantly better than that of several classical methods.

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“…A scale-aware convolutional neural network to restore a clear image was also proposed. Quan et al [10] advanced a deep-learning-based method to nonblind image deblurring, which combined Gabor-domain and complex-valued CNN-based prior to better handle the noise with unknown parameters or statistical characteristics. Liu et al [11] advanced a deblurring module to sharpen blur images of dynamic scenes based of high-frequency residual image learning.…”

Section: Introductionmentioning

confidence: 99%

An Image Deblurring Method Using Improved U-Net Model

Lian

Wang

Zhang

2022

Mobile Information Systems

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Deblurring methods in dynamic scenes are a challenging problem. Recently, significant progress has been made for image deblurring methods based on deep learning. However, these methods usually stack ordinary convolutional layers or increase convolution kernel size, resulting in limited receptive fields, an unsatisfying deblurring effect, and a heavy computational burden. Therefore, we propose an improved U-Net (U-shaped Convolutional Neural Network) model to restore the blurred images. We first design the model structure, which mainly includes depth-wise separable convolution, residual depth-wise separable convolution, wavelet transform, inverse wavelet transform, and a DMRFC (dense multireceptive field channel) module. Next, a depth-wise separable convolution is designed, which reduces model calculations and the number of parameters when compared with the standard convolution. A residual depth-wise separable convolution is designed, which allows for propagation of detailed information from different layers when compared with standard convolution and a standard residual block. The wavelet transform realizes downsampling by separating the contextual and texture information of the image. It also reduces model training difficulty. The inverse wavelet transform realizes upsampling, which reduces the loss of image information. Finally, by combining an extensional receptive field and channel attention mechanism, a DMRFC module is proposed to extract detailed image information, which further improves the reconstructed image quality via inverse wavelet transform. Experiments on the public dataset GOPRO show that the image deblurring method in this paper has higher-quality visual effects, while the PSNR (peak signal-to-noise ratio) rises to 30.83 and SSIM (structural similarity) rises to 0.948. Fewer model parameters and a shorter recovery time are needed, which provides a more lightweight image deblurring method.

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Nonblind Image Deblurring via Deep Learning in Complex Field

Cited by 23 publications

References 47 publications

High-Frequency Attention Residual GAN Network for Blind Motion Deblurring

High-Frequency Attention Residual GAN Network for Blind Motion Deblurring

Image Deblurring Algorithm Based on the Gaussian-Scale Mixture Expert Field Model

An Image Deblurring Method Using Improved U-Net Model

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