Optimizing exposure time for low light scenarios involves a trade-off between motion blur and signal to noise ratio. A method for defining the optimum exposure time for a given function has not been described in the literature. This paper presents the design of a simulation of motion blur and exposure time from the perspective of a real-world camera. The model incorporates characteristics of real-world cameras including the light level (quanta), shot noise and lens distortion. In our simulation, an image quality target chart called the Siemens Star chart will be used, and the simulation outputs a blurred image as if captured from a camera of set exposure and set movement speed. The resulting image is then processed in Imatest in which image quality readings will be extracted from the image and consequently the relationship between exposure time, motion blur and the image quality metrics can be evaluated.
Recently, with the release of 108 mega pixel resolution image sensor, the photo quality of smartphone camera, including detail, and texture, is getting much higher. This became possible only because by utilizing the remosaic technology which re-organize color filter arrays into the Bayer patterns compatible to existing Image Signal Processor (ISP) of commodity AP. However, the optimized parameter configurations of the remosaic block require lots of efforts and long tuning period in order to secure the desired image quality level and sensor characteristics. This paper proposes a deep neural network based camera auto-tuning system for the remosaic ISP block. Firstly, considering the learning phase, big image quality database is created in the random way using reference image and tuning register. Second, the virtual ISP model has been trained in order that predicts image quality by changing sensor tuning registers. Finally, the optimization layer generates the sensor remosaic parameters in order to achieve the user’s target image quality expectation. By experiment, the proposed system has been verified to secure the image quality at the level of professionally hand-tuned photography. Especially, the remosaic artifact of false color, color desaturation and line broken artifacts are improved significantly by more than 23%, 4%, and 12%, respectively.