HDR Image Construction from Trifocal Multiexposure Images

With the progress of autonomous vehicles, the sensing of the environment in more detail with higher dynamic ranges has become more important to classify surrounding objects and obstacles. While stereo HDR images for this purpose can provide advantages compared to the conventional LDR images, they suffer from limited dynamic ranges and spike-like noises due to the inaccuracies in disparity estimation. In this paper, we formulate the HDR image construction problem from trifocal multi-exposure images and develop a method which improves disparity estimation for better HDR image construction. Given the symmetric geometry of the trifocal setup, the proposed method uses the equivalence of disparities from middle to left and middle to right images to determine the reliable regions. The HDR radiance for the pixels in these reliable regions are estimated by using the weighted average of the warped images in different exposures and the middle image, whereas the radiance values outside the reliable regions are estimated by using only the middle image. The experiments with different exposure combinations for left, middle and right images reveal better performances of the proposed method compared to the stereo HDR imaging. It is also observed that the improvements are more apparent for larger disparities between the cameras.