Shiqi Chen

2023

1. 

   Computational Optics for Mobile Terminals in Mass Production

   Shiqi Chen, Ting Lin, Huajun Feng, and 3 more authors

   IEEE Transactions on Pattern Analysis and Machine Intelligence, Apr 2023

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   Correcting the optical aberrations and the manufacturing deviations of cameras is a challenging task. Due to the limitation on volume and the demand for mass production, existing mobile terminals cannot rectify optical degradation. In this work, we systematically construct the perturbed lens system model to illustrate the relationship between the deviated system parameters and the spatial frequency response (SFR) measured from photographs. To further address this issue, an optimization framework is proposed based on this model to build proxy cameras from the machining samples’ SFRs. Engaging with the proxy cameras, we synthetic data pairs, which encode the optical aberrations and the random manufacturing biases, for training the learning-based algorithms. In correcting aberration, although promising results have been shown recently with convolutional neural networks, they are hard to generalize to stochastic machining biases. Therefore, we propose a dilated Omni-dimensional dynamic convolution (DOConv) and implement it in post-processing to account for the manufacturing degradation. Extensive experiments which evaluate multiple samples of two representative devices demonstrate that the proposed optimization framework accurately constructs the proxy camera. And the dynamic processing model is well-adapted to manufacturing deviations of different cameras, realizing perfect computational photography. The evaluation shows that the proposed method bridges the gap between optical design, system machining, and post-processing pipeline, shedding light on the joint of image signal reception (lens and sensor) and image signal processing (ISP).

   @article{Chen_2022_TPAMI,
     author = {Chen, Shiqi and Lin, Ting and Feng, Huajun and Xu, Zhihai and Li, Qi and Chen, Yueting},
     journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
     title = {Computational Optics for Mobile Terminals in Mass Production},
     month = apr,
     year = {2023},
     volume = {45},
     number = {4},
     pages = {4245-4259},
   }

2021

1. 

   Extreme-Quality Computational Imaging via Degradation Framework

   Shiqi Chen, Huajun Feng, Keming Gao, and 2 more authors

   In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2021

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   To meet the space limitation of optical elements, free-form surfaces or high-order aspherical lenses are adopted in mobile cameras to compress volume. However, the application of free-form surfaces also introduces the problem of image quality mutation. Existing model-based deconvolution methods are inefficient in dealing with the degradation that shows a wide range of spatial variants over regions. And the deep learning techniques in low-level and physics-based vision suffer from a lack of accurate data. To address this issue, we develop a degradation framework to estimate the spatially variant point spread functions (PSFs) of mobile cameras. When input extreme-quality digital images, the proposed framework generates degraded images sharing a common domain with real-world photographs. Supplied with the synthetic image pairs, we design a Field-Of-View shared kernel prediction network (FOV-KPN) to perform spatial-adaptive reconstruction on real degraded photos. Extensive experiments demonstrate that the proposed approach achieves extreme-quality computational imaging and outperforms the state-of-the-art methods. Furthermore, we illustrate that our technique can be integrated into existing postprocessing systems, resulting in significantly improved visual quality.

   @inproceedings{Chen_2021_ICCV,
     author = {Chen, Shiqi and Feng, Huajun and Gao, Keming and Xu, Zhihai and Chen, Yueting},
     title = {Extreme-Quality Computational Imaging via Degradation Framework},
     booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
     month = oct,
     year = {2021},
     pages = {2632-2641},
   }

2. 

   Optical Aberrations Correction in Postprocessing Using Imaging Simulation

   Shiqi Chen, Huajun Feng, Dexin Pan, and 3 more authors

   ACM Trans. Graph. (Post.Rec in SIGGRAPH 2022), Sep 2021

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   As the popularity of mobile photography continues to grow, considerable effort is being invested in the reconstruction of degraded images. Due to the spatial variation in optical aberrations, which cannot be avoided during the lens design process, recent commercial cameras have shifted some of these correction tasks from optical design to postprocessing systems. However, without engaging with the optical parameters, these systems only achieve limited correction for aberrations.In this work, we propose a practical method for recovering the degradation caused by optical aberrations. Specifically, we establish an imaging simulation system based on our proposed optical point spread function model. Given the optical parameters of the camera, it generates the imaging results of these specific devices. To perform the restoration, we design a spatial-adaptive network model on synthetic data pairs generated by the imaging simulation system, eliminating the overhead of capturing training data by a large amount of shooting and registration.Moreover, we comprehensively evaluate the proposed method in simulations and experimentally with a customized digital-single-lens-reflex camera lens and HUAWEI HONOR 20, respectively. The experiments demonstrate that our solution successfully removes spatially variant blur and color dispersion. When compared with the state-of-the-art deblur methods, the proposed approach achieves better results with a lower computational overhead. Moreover, the reconstruction technique does not introduce artificial texture and is convenient to transfer to current commercial cameras.

   @article{Chen_2021_TOG,
     author = {Chen, Shiqi and Feng, Huajun and Pan, Dexin and Xu, Zhihai and Li, Qi and Chen, Yueting},
     title = {Optical Aberrations Correction in Postprocessing Using Imaging Simulation},
     year = {2021},
     issue_date = {October 2021},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     volume = {40},
     number = {5},
     issn = {0730-0301},
     url = {https://doi.org/10.1145/3474088},
     journal = {ACM Trans. Graph. (Post.Rec in SIGGRAPH 2022)},
     month = sep,
     articleno = {192},
     numpages = {15},
     keywords = {imaging simulation, deep-learning networks, image reconstruction, Optical aberrations},
   }