NFResNet: Multi-scale and U-shaped Networks for Deblurring

Mittal, Tanish; Agrawal, Preyansh; Pahwa, Esha; Makwana, Aarya

doi:10.48550/arxiv.2212.05909

Cited by 1 publication

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“…However, the traditional iterative algorithm often requires numerous iterations to achieve a satisfactory outcome, resulting in significant computational costs [25,26]. The second group comprises deep learning algorithms, which are designed to automatically learn features from the data, rather than relying on handcrafted features [4,5,25,[27][28][29][30][31]. For example, Tao et al [28] propose a new Scale-recurrent Network (SRN-DeblurNet) to deal with two problems in a CNN-based deblurring system.…”

Section: Introductionmentioning

confidence: 99%

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Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm

Wang,

Xu,

et al. 2023

Algorithms

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Image deblurring based on sparse regularization has garnered significant attention, but there are still certain limitations that need to be addressed. For instance, convex sparse regularization tends to exhibit biased estimation, which can adversely impact the deblurring performance, while non-convex sparse regularization poses challenges in terms of solving techniques. Furthermore, the performance of the traditional iterative algorithm also needs to be improved. In this paper, we propose an image deblurring method based on convex non-convex (CNC) sparse regularization and a plug-and-play (PnP) algorithm. The utilization of CNC sparse regularization not only mitigates estimation bias but also guarantees the overall convexity of the image deblurring model. The PnP algorithm is an advanced learning-based optimization algorithm that surpasses traditional optimization algorithms in terms of efficiency and performance by utilizing the state-of-the-art denoiser to replace the proximal operator. Numerical experiments verify the performance of our proposed algorithm in image deblurring.

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Section: Introductionmentioning

confidence: 99%

“…For example, Tao et al [28] propose a new Scale-recurrent Network (SRN-DeblurNet) to deal with two problems in a CNN-based deblurring system. Based on a new NFRes-block, Mittal et al [27] propose the NFResnet and NFResnet+. Zou et al [30] introduce a dilated convolution model (SDWNet) for image deblurring.…”

Section: Introductionmentioning

confidence: 99%

Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm

Wang,

Xu,

et al. 2023

Algorithms

View full text Add to dashboard Cite

show abstract

NFResNet: Multi-scale and U-shaped Networks for Deblurring

Cited by 1 publication

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Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm

Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm

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