  We present a novel method for inferring ground-truth signal from multiple
degraded signals, affected by different amounts of sensor exposure. The
algorithm learns a multiplicative degradation effect by performing iterative
corrections of two signals solely from the ratio between them. The degradation
function d should be continuous, satisfy monotonicity, and d(0) = 1. We use
smoothed monotonic regression method, where we easily incorporate the
aforementioned criteria to the fitting part. We include theoretical analysis
and prove convergence to the ground-truth signal for the noiseless measurement
model. Lastly, we present an approach to fuse the noisy corrected signals using
Gaussian processes. We use sparse Gaussian processes that can be utilized for a
large number of measurements together with a specialized kernel that enables
the estimation of noise values of all sensors. The data fusion framework
naturally handles data gaps and provides a simple and powerful method for
observing the signal trends on multiple timescales(long-term and short-term
signal properties). The viability of correction method is evaluated on a
synthetic dataset with known ground-truth signal.
