BASED: Benchmarking, Analysis, and Structural Estimation of Deblurring
Nikita Alutis Egor Chistov Mikhail Dremin Dmitriy Vatolin
This paper discusses the challenges of evaluating deblurring-methods quality
and proposes a reduced-reference metric based on machine learning. Traditional
quality-assessment metrics such as PSNR and SSIM are common for this task, but
not only do they correlate poorly with subjective assessments, they also
require ground-truth (GT) frames, which can be difficult to obtain in the case
of deblurring. To develop and evaluate our metric, we created a new motion-blur
dataset using a beam splitter. The setup captured various motion types using a
static camera, as most scenes in existing datasets include blur due to camera
motion. We also conducted two large subjective comparisons to aid in metric
development. Our resulting metric requires no GT frames, and it correlates well
with subjective human perception of blur.