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Abstract

We seek to reconstruct sharp and noise-free high-dynamic range (HDR) video
from a dual-exposure sensor that records different low-dynamic range (LDR)
information in different pixel columns: Odd columns provide low-exposure,
sharp, but noisy information; even columns complement this with less noisy,
high-exposure, but motion-blurred data. Previous LDR work learns to deblur and
denoise (DISTORTED->CLEAN) supervised by pairs of CLEAN and DISTORTED images.
Regrettably, capturing DISTORTED sensor readings is time-consuming; as well,
there is a lack of CLEAN HDR videos. We suggest a method to overcome those two
limitations. First, we learn a different function instead: CLEAN->DISTORTED,
which generates samples containing correlated pixel noise, and row and column
noise, as well as motion blur from a low number of CLEAN sensor readings.
Second, as there is not enough CLEAN HDR video available, we devise a method to
learn from LDR video in-stead. Our approach compares favorably to several
strong baselines, and can boost existing methods when they are re-trained on
our data. Combined with spatial and temporal super-resolution, it enables
applications such as re-lighting with low noise or blur.