Nowadays, mobile phone set makers are implementing a full screen display by changing the mounting form factor of the front camera, which is superior in design. When it comes to smart phone front-facing cameras, the hole type front-facing camera degrade the industrial design, while pop-up style camera has also limitation in terms of waterproofness and durability. In the case of Under Display Camera(UDC) which is in its final form factor by design, where a camera is placed underneath the screen, the display panel on the light-receiving path degrades the camera optical sensitivity and causes decrease in image quality performance due to regular display panel pattern. In order to commercialize the UDC, improving image quality of the UDC and exact measurement are crucial. However, subjectively evaluating image taken through the display panel is challenging task measure the image quality performance. This paper introduces a numeric based UDC image quantitative measurement method as a more objective evaluation way.
Can a mobile camera see better through display? Under Display Camera (UDC) is the most awaited feature in mobile market in 2020 enabling more preferable user experience, however, there are technological obstacles to obtain acceptable UDC image quality. Mobile OLED panels are struggling to reach beyond 20% of light transmittance, leading to challenging capture conditions. To improve light sensitivity, some solutions use binned output losing spatial resolution. Optical diffraction of light in a panel induces contrast degradation and various visual artifacts including image ghosts, yellowish tint etc. Standard approach to address image quality issues is to improve blocks in the imaging pipeline including Image Signal Processor (ISP) and deblur block. In this work, we propose a novel approach to improve UDC image quality - we replace all blocks in UDC pipeline with all-in-one network – UDC d^Net. Proposed solution can deblur and reconstruct full resolution image directly from non-Bayer raw image, e.g. Quad Bayer, without requiring remosaic algorithm that rearranges non-Bayer to Bayer. Proposed network has a very large receptive field and can easily deal with large-scale visual artifacts including color moiré and ghosts. Experiments show significant improvement in image quality vs conventional pipeline – over 4dB in PSNR on popular benchmark - Kodak dataset.
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.