With the invention of electric bulbs, human has been living under various illuminant environments. Since the alternative current (AC) power is used for supplier of electric bulbs, intensity difference between consecutive video frames can be captured with high-speed camera. While most of conventional methods focus on only spatial information of a single image, we propose a deep spatio-temporal color constancy method. To exploit the temporal feature from highspeed video, maximum gradient map is fed into the proposed network. It can easily identify image regions which are significantly illuminated by light bulbs and be a useful information for estimating the illuminant. The experimental results demonstrate that using temporal features leads to better performance of illuminant estimation rather than conventional spatial methods.

No abstract

No abstract

No abstract

This paper proposes a deep highlight removal method based on the dichromatic model under alternating current (AC) light sources with a new diffuse prior on temporal domain, temporal dark prior. An input image is decomposed into specular and diffuse components using a deep network. Due to AC powered lights, both incident and reflected lights are time-varying. We exploit the periodic variation property of the specular and diffuse reflections as a prior for dichromatic model based image decomposition. In addition, we propose a new temporal dark prior as a pseudo-diffuse reflection. Unlike the conventional prior in the spatial domain, to the best of our knowledge, this is the first study to utilize a diffuse prior on the temporal domain for highlight removal. The blurred version of the temporal dark prior is additionally fed to the network to alleviate hole artifacts. It is demonstrated through diverse experiments that these temporal priors can strongly contribute to accurate image decomposition, leading to better highlight removal. INDEX TERMS Dichromatic model, highlight removal, high-speed camera.The associate editor coordinating the review of this manuscript and approving it for publication was Yongjie Li.