  High screening coverage during colonoscopy is crucial to effectively prevent
colon cancer. Previous work has allowed alerting the doctor to unsurveyed
regions by reconstructing the 3D colonoscopic surface from colonoscopy videos
in real-time. However, the lighting inconsistency of colonoscopy videos can
cause a key component of the colonoscopic reconstruction system, the SLAM
optimization, to fail. In this work we focus on the lighting problem in
colonoscopy videos. To successfully improve the lighting consistency of
colonoscopy videos, we have found necessary a lighting correction that adapts
to the intensity distribution of recent video frames. To achieve this in
real-time, we have designed and trained an RNN network. This network adapts the
gamma value in a gamma-correction process. Applied in the colonoscopic surface
reconstruction system, our light-weight model significantly boosts the
reconstruction success rate, making a larger proportion of colonoscopy video
segments reconstructable and improving the reconstruction quality of the
already reconstructed segments.
