A map estimation framework for HDR video synthesis
High dynamic range (HDR) image synthesis from multiple low dynamic range (LDR) exposures continues to be a topic of great interest. The extension to HDR video comprises a stiff challenge due to significant motion. In particular, loss of data due to poor exposures introduces great difficulty in exact motion estimation, and under such circumstances conventional optical flow calculation techniques usually fail. We propose a maximum a posterior (MAP) estimation framework for HDR video synthesis algorithm free of explicit optical flow calculation. We formulate HDR video synthesis as a MAP estimation problem, which subsequently can be reduced to an optimization problem based on meaningful statistical assumptions on foreground and background regions of the input video. In the background regions the underlying scenes are static, while in the foreground regions motion information is captured implicitly by a modified 3D steering kernel regression (3D SKR) approach. Solution to the optimization problem provides us with temporally coherent HDR video sequences without noticeable artifacts. Experimental results on challenging LDR video sets demonstrate that our proposed algorithm can achieve HDR video quality that is competitive with or better than state of the art alternatives.