English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Deep Multi-Scale Feature Learning for Defocus Blur Estimation