Under Display Camera(UDC) technology is being developed to eliminate camera holes and place cameras behind display panels according to full display trend in mobile phone. However, these camera systems cause attenuation and diffraction as light passes through the panel, which is inevitable to deteriorate the camera image. In particular, the deterioration of image quality due to diffraction and flares is serious, in this regard, this paper discusses techniques for restoring it. The diffraction compensation algorithm in this paper is aimed at real-time processing through HW implementation in the sensor for preview and video mode, and we've been able to use effective techniques to reduce computation by about 40 percent.
Recent work in image deblurring aided by inertial sensor data has shown promise. Separate work has also shown that deep learning techniques are useful for the image deblurring problem. Due to a lack of a proper dataset, however, deep learning techniques have not yet to be successfully applied to image deblurring when inertial sensor data is also available. This paper proposes to generate a synthetic training and testing dataset that includes groundtruth and blurry image pairs as well as inertial sensor data recorded during the exposure time of each blurry image. To simulate the real situations, the proposed dataset called DeblurIMUDataset considers synchronization issue, rotation center shift, rolling shutter effect as well as inertial sensor data noise and image noise. This dataset is available online.