Extreme-Quality Computational Imaging via Degradation Framework

• Main Authors: Chen, Shiqi, Feng, Huajun, Gao, Keming, Xu, Zhihai, Chen, Yueting
• Format: Conference Proceeding
• Language: English
• Subjects: Cameras; Computational photography; Datasets and evaluation; Deconvolution; Degradation; Image and video synthesis; Image quality; Low-level and physics-based vision; Network architecture; Optical imaging; Visualization
• ISSN: 2380-7504
• DOI: 10.1109/ICCV48922.2021.00263

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.