Deep learning based projector defocus compensation in single-pixel imaging.

Fourier single-pixel imaging (FSI) uses a digital projector to illuminate the target with Fourier basis patterns, and captures the back-scattered light with a photodetector to reconstruct a high-quality target image. Like other single-pixel imaging (SPI) schemes, FSI requires the projector to be focused on the target for best performance. In case the projector lens is defocused, the projected patterns are blurred and their interaction with the target produces a low-quality image. To address this problem, we propose a fast, adaptive, and highly-scalable deep learning (DL) approach for projector defocus compensation in FSI. Specifically, we employ a deep convolutional neural network (DCNN), which learns to offset the effects of projector defocusing through training on a large image set reconstructed with varying defocus parameters. The model is further trained on experimental data to make it robust against system bias. Experimental results demonstrate the efficacy of our method in reconstructing high-quality images at high projector defocusing. Comparative results indicate the superiority of our method over conventional FSI and existing projector defocus rectification method. The proposed work can also be extended to other SPI methods influenced by projector defocusing, and open avenues for applying DL to correct optical anomalies in SPI.

DeepGhost: real-time computational ghost imaging via deep learning.

The potential of random pattern based computational ghost imaging (CGI) for real-time applications has been offset by its long image reconstruction time and inefficient reconstruction of complex diverse scenes. To overcome these problems, we propose a fast image reconstruction framework for CGI, called "DeepGhost", using deep convolutional autoencoder network to achieve real-time imaging at very low sampling rates (10-20%). By transferring prior-knowledge from STL-10 dataset to physical-data driven network, the proposed framework can reconstruct complex unseen targets with high accuracy. The experimental results show that the proposed method outperforms existing deep learning and state-of-the-art compressed sensing methods used for ghost imaging under similar conditions. The proposed method employs deep architecture with fast computation, and tackles the shortcomings of existing schemes i.e., inappropriate architecture, training on limited data under controlled settings, and employing shallow network for fast computation.

Deringing and denoising in extremely under-sampled Fourier single pixel imaging.

Undersampling in Fourier single pixel imaging (FSI) is often employed to reduce imaging time for real-time applications. However, the undersampled reconstruction contains ringing artifacts (Gibbs phenomenon) that occur because the high-frequency target information is not recorded. Furthermore, by employing 3-step FSI strategy (reduced measurements with low noise suppression) with a low-grade sensor (i.e., photodiode), this ringing is coupled with noise to produce unwanted artifacts, lowering image quality. To improve the imaging quality of real-time FSI, a fast image reconstruction framework based on deep convolutional autoencoder network (DCAN) is proposed. The network through context learning over FSI artifacts is capable of deringing, denoising, and recovering details in 256 × 256 images. The promising experimental results show that the proposed deep-learning-based FSI outperforms conventional FSI in terms of image quality even at very low sampling rates (1-4%).

Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning.

Fourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI, a fast image reconstruction framework based on deep learning (DL) is proposed. More specifically, a deep convolutional autoencoder network with symmetric skip connection architecture for real time 96 × 96 imaging at very low sampling rates (5-8%) is employed. The network is trained on a large image set and is able to reconstruct diverse images unseen during training. The promising experimental results show that the proposed FSPI coupled with DL (termed DL-FSPI) outperforms conventional FSPI in terms of image quality at very low sampling rates.

Design of a retina-like transmitting system based on a curved lens array.

In order to reduce data redundancy and improve scanning efficiency in three-dimensional imaging, a retina-like transmitting optical system based on a curved lens array (CLA) is proposed. The mathematical model for the transmitting system is developed, and associated parameters for the space-variant CLA are studied. Model testing validates that the transmitting system bears flexible scanning field of view and achieves scanning efficiency up to 80%. Imaging simulations show that the proposed scanning method utilizing the logarithmic-polar imaging characteristics efficiently detects secondary targets. Furthermore, simulation results show that the parameters of the single lens in the lens array are not fixed, but flexible, which facilitates the corresponding structural design.