Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

Jiao, Shuming; Jin, Zhi; Chang, Chenliang; Zhou, Changyuan; Zou, Wenbin; Li, Xia

doi:10.3390/app8081258

Cited by 60 publications

(24 citation statements)

References 56 publications

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“…In recent years, deep learning receives much attention in many research fields including optical design [1,2] and optical imaging [3]. In previous works, deep learning has been extensively applied for many optical imaging problems including phase retrieval [4][5][6][7], microscopic image enhancement [8][9], scattering imaging [10][11], holography [12][13][14][15][16][17][18], single-pixel imaging [19,20], super-resolution [21][22][23][24], Fourier ptychography [25][26][27], optical interferometry [28,29], wavefront sensing [30,31], and optical fiber communications [32].…”

Section: Introductionmentioning

confidence: 99%

Does deep learning always outperform simple linear regression in optical imaging?

et al. 2020

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Deep learning has been extensively applied in many optical imaging problems in recent years. Despite the success, the limitations and drawbacks of deep learning in optical imaging have been seldom investigated. In this work, we show that conventional linear-regression-based methods can outperform the previously proposed deep learning approaches for two black-box optical imaging problems in some extent. Deep learning demonstrates its weakness especially when the number of training samples is small. The advantages and disadvantages of linear-regression-based methods and deep learning are analyzed and compared. Since many optical systems are essentially linear, a deep learning network containing many nonlinearity functions sometimes may not be the most suitable option.

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Section: Introductionmentioning

confidence: 99%

Does deep learning always outperform simple linear regression in optical imaging?

et al. 2020

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“…In this deep learning approach, the author proposed a noniterative method using ResNet to generated the holograms and then compared it with the iterative method (Gerchberg-Saxton). In [15], to the best of our knowledge, the authors presented a method to employ deep neural networks to reduce losses in a JPEG compressed hologram, as the reconstructed image of JPEG compressed hologram suffers from extreme quality loss because, during the compression process, certain high-frequency features in the hologram will be lost. e proposed framework in [16] is called deep learning invariant hologram classification (DL-IHC) and the authors introduced a self-attention to convolution neural network (CNN) to implement a classification hologram of deformable objects.…”

Section: Literature Reviewmentioning

confidence: 99%

GAN-Holo: Generative Adversarial Networks-Based Generated Holography Using Deep Learning

Khan

Zhang

et al. 2021

Complexity

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Current development in a deep neural network (DNN) has given an opportunity to a novel framework for the reconstruction of a holographic image and a phase recovery method with real-time performance. There are many deep learning-based techniques that have been proposed for the holographic image reconstruction, but these deep learning-based methods can still lack in performance, time complexity, accuracy, and real-time performance. Due to iterative calculation, the generation of a CGH requires a long computation time. A novel deep generative adversarial network holography (GAN-Holo) framework is proposed for hologram reconstruction. This novel framework consists of two phases. In phase one, we used the Fresnel-based method to make the dataset. In the second phase, we trained the raw input image and holographic label image data from phase one acquired images. Our method has the capability of the noniterative process of computer-generated holograms (CGHs). The experimental results have demonstrated that the proposed method outperforms the existing methods.

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“…The deep neural networks can solve many remaining problems in fields of scattering imaging, computational ghost imaging, Fourier laminar microscopic imaging, phase retrieval and hologram data compression etc. [5][6][7][8][9][10][11]. It is shown that as an object passes through a scattering medium or multimode fiber (MMF), the information of the image is deteriorated.…”

Section: Introductionmentioning

confidence: 99%

Mutual Transfer Learning of Reconstructing Images Through a Multimode Fiber or a Scattering Medium

Lai

et al. 2021

IEEE Access

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In recent years, convolutional neural network (CNN) has been successfully applied to reconstruct image from the speckle, which is generated as an object passes through a scattering medium or a multimode fiber (MMF). To reconstruct image from the speckle, the CNN must be trained with a large number of object-speckle pairs (training dataset), and the trained CNN is capable of reconstructing image from dataset (test dataset), which is taken in the same condition as the training dataset. However, in some cases, data type and the scattering medium may vary with the situation. In this case, the CNN has to be retrained using a large number of new data taken from the new scattering media for reconstructing image. In this paper, we develop a CNN called as Mobiledense-net (MDN) to realize the mutual transfer learning. Specifically, the MDN is first pre-trained with a large number of object-speckle pairs taken from MMF or scattering slab, then tuned with quite small number of object-speckle pairs from scattering slab or MMF. It is shown that in this case the MDN can reconstruct image from the speckle with quite good quality, in which the speckle is taken from MMF or the scattering slab. We also show that using a more complex dataset for pre-training, the amount of data for pre-training can be largely reduced and reconstruction quality can be further improved. Using transfer learning, the reconstruction quality is quite good, being up to 99% . The results in this paper provide a more generalized method for studying the imaging through scattering imaging or MMF by using CNN.INDEX TERMS Imaging, convolutional neural network (CNN), Mobiledense-net (MDN), transfer learning, scattering medium, multimode fiber (MMF).

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Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

Cited by 60 publications

References 56 publications

Does deep learning always outperform simple linear regression in optical imaging?

Does deep learning always outperform simple linear regression in optical imaging?

GAN-Holo: Generative Adversarial Networks-Based Generated Holography Using Deep Learning

Mutual Transfer Learning of Reconstructing Images Through a Multimode Fiber or a Scattering Medium

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