Defocus Estimation
3 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Defocus Estimation
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CUHK03
Motion Blurred and Defocused Dataset
Latest papers with no code
Multi-task Learning for Monocular Depth and Defocus Estimations with Real Images
We set up a dataset (named ALL-in-3D dataset) which is the first all-real image dataset consisting of 100K sets of all-in-focus images, focused images with focus depth, depth maps, and defocus maps.
DeFusionNET: Defocus Blur Detection via Recurrently Fusing and Refining Multi-Scale Deep Features
After that, the fused shallow features are propagated to top layers for refining the fine details of detected defocus blur regions, and the fused semantic features are propagated to bottom layers to assist in better locating the defocus regions.
Enhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network
Specifically, we design an end-to-end network composed of two logical parts: feature extractor network (FENet) and defocus blur detector cross-ensemble network (DBD-CENet).
Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network
To address these issues, we propose a multi-stream bottom-top-bottom fully convolutional network (BTBNet), which is the first attempt to develop an end-to-end deep network for DBD.
Convergence Analysis of MAP based Blur Kernel Estimation
One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image.
