2021
DOI: 10.21203/rs.3.rs-209796/v1
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Holographic Optical Field Recovery Using a Regularized Untrained Deep Decoder Network

Abstract: Image reconstruction using minimal measured information has been a long-standing open problem in many computational imaging approaches, in particular in-line holography. Many solutions are devised based on compressive sensing (CS) techniques with handcrafted image priors or supervised Deep Neural Networks (DNN). However, the limited performance of CS methods due to lack of information about the image priors and the requirement of an enormous amount of per-sample-type training resources for DNNs has posed new c… Show more

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Cited by 2 publications
(13 citation statements)
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“…The results show that APGen outperforms existing data-driven and model-based in terms of time efficiency, reconstruction performance, and robustness to action potential misalignment overlap. Similarly, in [45], the authors showed that under-parameterized UNNPs integrated with simple regularizers (such as energy minimization or l 2 regularization) can be used for the reconstruction of Gabor holograms. In their proposed UNNP framework, the input random noise was kept constant.…”
Section: Craftingmentioning
confidence: 98%
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“…The results show that APGen outperforms existing data-driven and model-based in terms of time efficiency, reconstruction performance, and robustness to action potential misalignment overlap. Similarly, in [45], the authors showed that under-parameterized UNNPs integrated with simple regularizers (such as energy minimization or l 2 regularization) can be used for the reconstruction of Gabor holograms. In their proposed UNNP framework, the input random noise was kept constant.…”
Section: Craftingmentioning
confidence: 98%
“…[27] [28] [29], [30], [31], [32] [33], [ [36], [37] [38], [39], [40], [41], [42], [43] [44] [45] [46] [47], [48], [49], [50] [51] [52], [53], [54] [ 55] image (in vectorized form) x 0 ∈ R n is observed via m noisy measurements y ∈ R m . More specifically, we can write…”
mentioning
confidence: 99%
“…recently to create unsupervised learning frameworks [5,27,28]. Most of these frameworks utilize CNN architectures as their backbones since they can capture sufficient low-level image features to reproduce uncorrupted and realistic image parts [29].…”
Section: Related Work On Deep Learning-based Dhmentioning
confidence: 99%
“…For example, our previous work [5] uses an hourglass encoder-decoder structure to reconstruct the object wave from DIH holograms. Inspired by the Deep decoder concept proposed in [27], the reconstruction algorithm in [28] abandoned the encoder part and only used a decoder with a fixed random tensor as its input. Some classical regularization methods, such as total variation (TV) loss and weight decay, are applied to partially solve the noisy and incomplete signal problem.…”
Section: Related Work On Deep Learning-based Dhmentioning
confidence: 99%
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