Deep Multi-Scale Feature Learning for Defocus Blur Estimation

This paper presents an edge-based defocus blur estimation method from a single defocused image. We first distinguish edges that lie at depth discontinuities (called
depth
edges, for which the blur estimate is ambiguous) from edges that lie at approximately constant depth regions (called
pattern
edges, for which the blur estimate is well-defined). Then, we estimate the defocus blur amount at
pattern
edges only, and explore an interpolation scheme based on guided filters that prevents data propagation across the detected
depth
edges to obtain a dense blur map with well-defined object boundaries. Both tasks (edge classification and blur estimation) are performed by deep convolutional neural networks (CNNs) that share weights to learn meaningful local features from multi-scale patches centered at edge locations. Experiments on naturally defocused images show that the proposed method presents qualitative and quantitative results that outperform state-of-the-art (SOTA) methods, with a good compromise between running time and accuracy.