Image Deconvolution with Deep Image and Kernel Priors

Wang, Zhunxuan; Wang, Zipei; Bilen, Hakan

doi:10.1109/iccvw.2019.00127

Cited by 12 publications

(10 citation statements)

References 39 publications

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“…Image Deconvolution [113] Inspired by learning free deconvolution methods, modified classical UNNP's objective to deconvolution energy function.…”

Section: Compressed Sensingmentioning

confidence: 99%

“…Wang et al [113] combined the idea of learning-free deconvolution methods with neural networks and proposed a variant of UNNP named deep image kernel prior (DIKP), in which they modified the objective function of classical UNNP to deconvolution energy function. They showed that DIKP improves the performance of image deconvolution and outperforms traditional learning-free regularizationbased priors for image deconvolution.…”

Section: Image Deconvolutionmentioning

confidence: 99%

See 1 more Smart Citation

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Qayyum

Ilahi

Shamshad

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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In recent years, advancements in machine learning (ML) techniques, in particular, deep learning (DL) methods have gained a lot of momentum in solving inverse imaging problems, often surpassing the performance provided by hand-crafted approaches. Traditionally, analytical methods have been used to solve inverse imaging problems such as image restoration, inpainting, and superresolution. Unlike analytical methods for which the problem is explicitly defined and the domain knowledge is carefully engineered into the solution, DL models do not benefit from such prior knowledge and instead make use of large datasets to predict an unknown solution to the inverse problem. Recently, a new paradigm of training deep models using a single image, named untrained neural network prior (UNNP) has been proposed to solve a variety of inverse tasks, e.g., restoration and inpainting. Since then, many researchers have proposed various applications and variants of UNNP. In this paper, we present a comprehensive review of such studies and various UNNP applications for different tasks and highlight various open research problems which require further research.

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“…Image Deconvolution [113] Inspired by learning free deconvolution methods, modified classical UNNP's objective to deconvolution energy function.…”

Section: Compressed Sensingmentioning

confidence: 99%

Section: Image Deconvolutionmentioning

confidence: 99%

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Qayyum

Ilahi

Shamshad

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

show abstract

“…Different CNN-architectures such as Scale Recurrent Network (SRN) [35], Deep-Deblur [36], MPR-Net [16] have achieved impressive performance on the task of single image deblurring without explicitly incorporating assumptions about uniform/non-uniform motion blur. There are learning based methods such as [37], [38], [39], [40] which take the forward imaging model into consideration. For example, [37] learns the Fourier coefficients of the deconvolution filter from patches of image and deconvolves the blurred image by applying the patchwise average of the predicted filter.…”

Section: Related Workmentioning

confidence: 99%

Photon-Limited Blind Deconvolution using Unsupervised Iterative Kernel Estimation

Sanghvi¹,

Gnanasambandam²,

Mao³

et al. 2022

Preprint

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Blind deconvolution in low-light is one of the more challenging problems in image restoration because of the photon shot noise. However, existing algorithms -both classical and deep-learning based -are not designed for this condition. When the shot noise is strong, conventional deconvolution methods fail because (1) the presence of noise makes the estimation of the blur kernel difficult; (2) generic deep-restoration models rarely model the forward process explicitly; (3) there are currently no iterative strategies to incorporate a non-blind solver in a kernel estimation stage. This paper addresses these challenges by presenting an unsupervised blind deconvolution method. At the core of this method is a reformulation of the general blind deconvolution framework from the conventional image-kernel alternating minimization to a purely kernel-based minimization. This kernel-based minimization leads to a new iterative scheme that backpropagates an unsupervised loss through a pre-trained non-blind solver to update the blur kernel. Experimental results show that the proposed framework achieves superior results than state-of-the-art blind deconvolution algorithms in low-light conditions.

show abstract

“…Wang et al [104] combined the idea of learning-free deconvolution methods with neural networks and proposed a variant of UNNP named deep image kernel prior (DIKP). They showed that DIKP improves the performance of image deconvolution and outperforms traditional learningfree regularization-based priors for image deconvolution.…”

Section: Image Deconvolutionmentioning

confidence: 99%