Efficient Framework for Real-Time Color Cast Correction and Dehazing Using Online Algorithms to Approximate Image Statistics

Image dehazing is a crucial task in the early stages of real-time image processing pipelines used in applications such as surveillance and advance driver assistance systems. Dehazing algorithms mitigate the spatially selective degradation of image details caused by natural phenomenon such as fog and sandstorms. The problem is exacerbated in the presence of color cast which can affect the color-sensitive processing in the downstream tasks. In order to correct these two aberrations, we have proposed a light-weight algorithm which is not only quantitively more effective than the state-of-the-art works but also uses minimal computational resources. Specifically, it has been proposed to tackle both aforementioned problems by processing luminance and chrominance channels separately through a custom resource-efficient colorspace transform. Moreover, it has been proposed to employ online calculation of the relevant video stream statistics to estimate the degradation model over several temporally adjacent frames. This approach not only reduces the hardware resource utilization when implemented inside the video processing pipeline but also reduces the flicker effect observed when frames are processed individually. It has been demonstrated through quantitative analysis on standard datasets that the proposed approach either works at par or better than the reference works in terms of image quality metrics. Furthermore, the proposed framework has been developed as a real-time video processing system on an FPGA platform. The synthesis results of this implementation suggest that the proposed framework achieves this performance using minimal logic resources.