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...

Ghost imaging (GI) is an unconventional optical imaging method making use of the correlation measurement between a test beam and a reference beam. GI using deep learning (GIDL) has earned increasing attention, as it can reconstruct images of high quality more effectively than traditional GI methods. It has been demonstrated that GIDL can be trained completely with simulation data, which makes it even more practical. However, most GIDLs proposed so far appear to have limited performance for random noise distributed patterns. This is because traditional GIDLs are sensitive to the under-estimation error but robust to the over-estimation error. An asymmetric learning framework is proposed here to tackle the unbalanced sensitivity to estimation errors of GIDL. The experimental results show that it can achieve much better reconstructed images than GIDL with a symmetric loss function, and the structural similarity index of GI is quadrupled for randomly selected objects.

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