Recovering an object only from the amplitude of its Fourier measurement is a long-standing challenge. To confront this intricate challenge of illness more effectively, we propose a framework that combines data-driven pre-training and physics-driven iteration. These constraints including adapted support region and noise of image, which comes from the feature of object itself. Our analysis of both simulated and optical experiments data reveals that this framework offers superior results than other methods. Moreover, this improvement is achieved without suffering from the limitation of the dataset, may cast new light on network based algorithm in the future.
Single-pixel imaging (SPI) holds significant promise for addressing specialized imaging challenges, particularly in scenarios involving unconventional wavelength ranges and low-light conditions. Recent developments in SPI employing a spinning mask have successfully addressed the limitations of traditional modulators like the Digital Micromirror Device (DMD), particularly concerning refresh ratios and operational spectral bands. However, current spinning mask implementations, relying on random patterns or cyclic Hadamard patterns, struggle to achieve rapid and high-fidelity imaging when operating at low sampling ratios. Here we propose to use deep learning to jointly optimize the encoding and decoding scheme for spinning mask-based SPI. On the encoding side, a cyclic mask, optimized by the convolutional layer, is meticulously crafted to modulate the input object. On the decoding side, the object image is reconstructed from the modulated intensity fluctuations employing a lightweight neural network infused with the image formation model. Our method demonstrates the potential to achieve remarkable imaging results, generating 71×73-pixel images using only a 4% sampling ratio while maintaining a 2.4MHz modulation ratio, yielding image recording speeds surpassing 12KHz. The proposed method dramatically improves the imaging efficiency of SPI, thereby accelerating the practical utilization of SPI in domains such as specialized wavelength imaging and high-speed imaging.
Deep neural networks have been successfully applied to constrained object priors and parameter optimization. Here, we propose a novel learning-free deep neural network architecture to tackle uncertain system optimization. This blind system constraint deep neural network(BlindNet) need not to know all the parameters of the system and can simultaneously acquire the desired image and system parameters. In order to do so, we showed that the BlindNet can perform phase retrieval on the diffraction pattern with unknown diffraction distance.
Conventional digital holography (DH) technique largely limited by the effect of random scattering media in the imaging path, which causes great challenges for its applications in vivo imaging. As an improvement, short-coherence digital holography (SCDH) uses a low-coherence light source (near-infrared (NIR) region), where the absorption of light is at a minimum, to enhance its ability to resist scattering. However, SCDH also fails under strong scattering conditions. Here we propose to use deep learning (DL) for SCDH, and the results show that an image of a target behind a 2.30 mm chicken breast tissue can be reconstructed successfully. We experimentally demonstrate that DL-based SCDH can be used to reconstruct the object from a single measurement under some hard conditions, for example, when there is strong static or dynamic scattering media in the imaging path.
It is well known that neural networks including deep learning have been widely employed to solve the problems in recognition and classification. It was not until recently that people started to use them to solve imaging problems. In this talk, we focus on how to use deep learning to solve phase retrieval problems.
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