An underwater vision system operating in shallow water faces unique challenges, which often degrade the quality of the acquired data. One of these challenges is the sunflicker effect, created from refracted sunlight casting fast moving patterns on the seafloor. Surprisingly few previous works exist to address this topic. The best performing available method mitigates the sunflickering effect using offline motion compensated filtering. In the present work, we propose an online sunflicker removal method targeted at producing better registration accuracy. The illumination field of the sunflicker effect is considered as a dynamic texture, since it produces repetitive dynamic patterns. With that assumption, the dynamic model of the sunflicker is learned from the registered illumination fields of the previous frames and is used for predicting that of the next coming frame. Such prediction allows for removing the sunflicker patterns from the new frame and successfully register it against previous frames. Comparative results are presented using challenging test sequences which illustrate the better performance of the approach against the closest related method in the literature.
The same authors later extend their work in REF and propose an online sunflicker removal method which treats caustics as a dynamic texture.
ONLINE SUNFLICKER REMOVAL USING DYNAMIC TEXTURE PREDICTION
VISAPP
Computer Science