Images captured at low light suffers from underexposure and noise. These poor-quality images act as hindrance for computer vision algorithms as well as human vision. While this problem can be solved by increasing the exposure time, it also introduces new problems. In applications like ADAS, where there are fast moving objects in the scene, increasing the exposure time will cause motion blur. In applications, that demand higher frame rate, increasing the exposure time is not an option. Increasing the gain will result in noise as well as saturation of pixels at higher end. So, a real time scene adaptive algorithm is required for the enhancement of low light images. We propose a real time low light enhancement algorithm with more detail preservation compared to existing global based enhancement algorithms for low cost embedded platforms. The algorithm is integrated to image signal processing pipeline of TI’s TDA3x and achieved ˜50fps on c66x DSP for HD resolution video captured from Omnivision’s OV10640 Bayer image sensor.
Automated Driving requires fusing information from multitude of sensors such as cameras, radars, lidars mounted around car to handle various driving scenarios e.g. highway, parking, urban driving and traffic jam. Fusion also enables better functional safety by handling challenging scenarios such as weather conditions, time of day, occlusion etc. The paper gives an overview of the popular fusion techniques namely Kalman filters and its variation e.g. Extended Kalman filters and Unscented Kalman filters. The paper proposes choice of fusing techniques for given sensor configuration and its model parameters. The second part of paper focuses on efficient solution for series production using embedded platform using Texas Instrument's TDAx Automotive SoC. The performance is benchmarked separately for "predict" and "update" phases on for different sensor modalities. For typical L3/L4 automated driving consisting of multiple cameras, radars and lidars, fusion can supported in real time by single DSP using proposed techniques enabling cost optimized solution.