Title: Bridging the Gap Between Computational Photography and Visual Recognition,
Abstract: What is the current state-of-the-art for image restoration and enhancement
applied to degraded images acquired under less than ideal circumstances? Can
the application of such algorithms as a pre-processing step to improve image
interpretability for manual analysis or automatic visual recognition to
classify scene content? While there have been important advances in the area of
computational photography to restore or enhance the visual quality of an image,
the capabilities of such techniques have not always translated in a useful way
to visual recognition tasks. Consequently, there is a pressing need for the
development of algorithms that are designed for the joint problem of improving
visual appearance and recognition, which will be an enabling factor for the
deployment of visual recognition tools in many real-world scenarios. To address
this, we introduce the UG^2 dataset as a large-scale benchmark composed of
video imagery captured under challenging conditions, and two enhancement tasks
designed to test algorithmic impact on visual quality and automatic object
recognition. Furthermore, we propose a set of metrics to evaluate the joint
improvement of such tasks as well as individual algorithmic advances, including
a novel psychophysics-based evaluation regime for human assessment and a
realistic set of quantitative measures for object recognition performance. We
introduce six new algorithms for image restoration or enhancement, which were
created as part of the IARPA sponsored UG^2 Challenge workshop held at CVPR
2018. Under the proposed evaluation regime, we present an in-depth analysis of
these algorithms and a host of deep learning-based and classic baseline
approaches. From the observed results, it is evident that we are in the early
days of building a bridge between computational photography and visual
recognition, leaving many opportunities for innovation in this area.