Estimation of ambient light and transmission map with common convolutional architecture
Young-Sik Shin, Younggun Cho, Gaurav Pandey, Ayoung Kim · 2016
This paper presents a method for effective ambient light and transmission estimation in underwater images using a common convolutional network architecture. The estimated ambient light and the transmission map are used to dehaze the underwater images. Dehazing underwater images is especially challenging due to the unknown and significantly varying ambient light in underwater environments. Unlike common dehazing methods, the proposed method is capable of estimating ambient light along with the transmission map thereby improving the reconstruction quality of the dehazed images. We evaluate the dehazing performance of the proposed method on real underwater images and also compare our method to current state-of-the-art techniques.