Blurry-Edges: Photon-Limited Depth Estimation from Defocused Boundaries
Journal:
arXiv
Published Date:
Mar 30, 2025
Abstract
Extracting depth information from photon-limited, defocused images is
challenging because depth from defocus (DfD) relies on accurate estimation of
defocus blur, which is fundamentally sensitive to image noise. We present a
novel approach to robustly measure object depths from photon-limited images
along the defocused boundaries. It is based on a new image patch
representation, Blurry-Edges, that explicitly stores and visualizes a rich set
of low-level patch information, including boundaries, color, and smoothness. We
develop a deep neural network architecture that predicts the Blurry-Edges
representation from a pair of differently defocused images, from which depth
can be calculated using a closed-form DfD relation we derive. The experimental
results on synthetic and real data show that our method achieves the highest
depth estimation accuracy on photon-limited images compared to a broad range of
state-of-the-art DfD methods.
