High Dynamic Range Imaging Using Deep Image Priors

Jagatap, Gauri; Hegde, Chinmay

doi:10.1109/icassp40776.2020.9054218

Cited by 12 publications

(17 citation statements)

References 28 publications

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“…While in this paper we considered only two modulo periods, extending the proposed approach for more periods (up to a theoretically infinite number) is a significant and interesting research direction. Instead of relying on sparsity prior for compressed recovery, employing novel set of priors such as GAN priors [39][40][41] is an additional direction. Moreover, our analysis is limited to the case of Gaussian measurements schemes, which may or may not be physically realizable.…”

Section: Discussionmentioning

confidence: 99%

Sparse Signal Recovery from Modulo Observations

Shah

Hegde

2020

Preprint

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We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have also been studied under the category of phase unwrapping. Signal reconstruction in the under-determined regime with modulo observations is a challenging ill-posed problem, and existing reconstruction methods cannot be used directly. In this paper, we propose a novel approach to solving the inverse problem limited to two modulo periods, inspired by recent advances in algorithms for phase retrieval under sparsity constraints. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal and provides improved performance over other existing algorithms. We also provide experiments validating our approach on both synthetic and real data to depict its superior performance.

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Section: Discussionmentioning

confidence: 99%

Sparse Signal Recovery from Modulo Observations

Shah

Hegde

2020

Preprint

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“…Gradient descent-based methods are applied to optimize θ to produce a favorable restored image x * . Because of the generalizability of the CNN, DIP is a universal prior that can be easily applied to nearly all image processing tasks-SR [168], deblurring [170], dehazing [169], denoising [176], HDR [177], to name a few.…”

Section: Deep Image Priormentioning

confidence: 99%

All One Needs to Know about Priors for Deep Image Restoration and Enhancement: A Survey

Lu¹,

Lin²,

Wu³

et al. 2022

Preprint

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Image restoration and enhancement is a process of improving the image quality by removing degradations, such as noise, blur, and resolution degradation. Deep learning (DL) has recently been applied to image restoration and enhancement. Due to its ill-posed property, plenty of works have explored priors to facilitate training deep neural networks (DNNs). However, the importance of priors has not been systematically studied and analyzed by far in the research community. Therefore, this paper serves as the first study that provides a comprehensive overview of recent advancements of priors for deep image restoration and enhancement. Our work covers five primary contents: (1) A theoretical analysis of priors for deep image restoration and enhancement; (2) A hierarchical and structural taxonomy of priors commonly used in the DL-based methods; (3) An insightful discussion on each prior regarding its principle, potential, and applications; (4) A summary of crucial problems by highlighting the potential future directions to spark more research in the community; ( 5) An open-source repository that provides a taxonomy of all mentioned works and code links.

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“…This enhancement can be performed by extending (post-acquisition) the dynamic range of the captured images. Jagatap et al [49] proposed the use of UNNP for high dynamic range (HDR) image reconstruction without training data. They proposed a combination of UNNP and TV regularization to reconstruct low-light images.…”

Section: Image Enhancementmentioning

confidence: 99%