A residual-based deep learning approach for ghost imaging

Abstract

Ghost imaging using deep learning (GiDL) is a kind of computational quantum imaging method devised to improve the imaging efficiency. However, among most proposals of GIDL so far, the same set of random patterns were used in both the training and test set, leading to a decrease of the generalization ability of networks. Thus, the GIDL technique can only reconstruct the profile of the image of the object, losing most of the details. Here we optimize the simulation algorithm of ghost imaging (GI) by introducing the concept of "batch" into the pre-processing stage. It can significantly reduce the data acquisition time and create reliable simulation data. The generalization ability of GIDL has been appreciably enhanced. Furthermore, we develop a residual-based framework for the GI system, namely the double residual U-Net (DRU-Net). The imaging quality of GI has been tripled in the evaluation of the structural similarity index by our proposed DRU-Net. Ghost imaging (GI) is an unconventional imaging method compared with traditional optical imaging methods. It was first proposed as a quantum entangled phenomenon by making use of the entangled two-photon light source generated by the spontaneous parameter down-conversion (SPDC) process 1. However, GI was demonstrated to be accomplished by classical incoherent light source soon later 2. It led to controversy focus on whether the quantum entangled is necessary for the GI system. The GI system contains two distributed light beams, which are the test beam and the reference beam. In the test beam, the light illuminates the object directly and then be collected into a bucket measurement. In the reference beam, the light travels freely to a high-resolution detector without interacting with the object. The image can then be reconstructed through the correlation measurement between the two light beam signals 3. In terms of computational ghost imaging (CGI), the reference beam becomes unnecessary as one can obtain the image by calculating the correlation between the test beam intensity and the knowledge of the random patterns displayed on the spatial light modulator (SLM) 4-6. CGI usually needs a large number of random patterns to avoid noise disturbance to achieve images of high resolution. This requirement leads to a long data acquisition time, which is the main issue preventing CGI from practical use. Improved correlation method such as normalized ghost imaging 7 and differential ghost imaging 8 was later proposed to increase imaging efficiency. Notably, the Gerchberg-Saxton algorithm and compressive sensing ghost imaging (CSGI) regard GI as an optimization problem. In 9 , the Gerchberg-Saxton-like technique takes the integral property of Fourier transform into full consideration to provide a different perspective of image reconstruction of GI. CSGI can reduce measurement times and boost imaging quality 10-14. Recently, deep learning (DL) has achieved widespread use in various fields, such as image denoising, image inpainting, natural language processing, to name a few 15. DL h...

Derivative ghost imaging

“…Compressed sensing algorithms produce high-quality images at low sampling rates [29,30] . The combination of GI and deep learning has recently been shown to improve image quality and reduce sampling times [31][32][33][34][35] .…”

SP-ILC: Concurrent Single-Pixel Imaging, Object Location, and Classification by Deep Learning

“…Many advances in deep learning have been made recently in, for example, image classification [20], object detection [21], and image segmentation [22][23][24]. Deep learning has been found to improve the quality of single-pixel imaging and to reduce the impact of noise on imaging; it has also been used for single-pixel object classification [25][26][27][28][29][30][31][32].…”

Optimized Binary Patterns by Gradient Descent for Ghost Imaging

“…In recent years, deep learning has achieved great successes in image noise reduction [16] and super-resolution [17]. The most common examples of applying deep neural networks to ghost imaging in previous studies are both refining the image reconstructed by correlation calculation with deep neural network [18], [19], and directly reconstructing the image with networks without using correlation calculations [20]. However, these methods have some disadvantages, such as additional and high computational load and decreased noise robustness which is a feature of ghost imaging.…”