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Single image defocus blur segmentation using Local Ternary Pattern

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1 Published in 2020 at "ICT Express"

DOI: 10.1016/j.icte.2019.10.003

Abstract: Abstract This work presents an efficient LTP-based sharpness measure for blur detection and segmentation. The proposed method transforms each pixel into ternary codes depending on the differences of intensity of the central pixel with the… read more here.

Keywords: blur segmentation; segmentation; image defocus; defocus blur ... See more keywords

Defocus Blur Detection by Fusing Multiscale Deep Features With Conv-LSTM

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1 Published in 2020 at "IEEE Access"

DOI: 10.1109/access.2020.2996200

Abstract: Defocus blur detection aiming at distinguishing out-of-focus blur and sharpness has attracted considerable attention in computer vision. The present blur detectors suffer from scale ambiguity, which results in blur boundaries and low accuracy in blur… read more here.

Keywords: defocus blur; blur detection; blur; multiscale deep ... See more keywords

Image-Scale-Symmetric Cooperative Network for Defocus Blur Detection

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1 Published in 2022 at "IEEE Transactions on Circuits and Systems for Video Technology"

DOI: 10.1109/tcsvt.2021.3095347

Abstract: Defocus blur detection (DBD) for natural images is a challenging vision task especially in the presence of homogeneous regions and gradual boundaries. In this paper, we propose a novel image-scale-symmetric cooperative network (IS2CNet) for DBD.… read more here.

Keywords: network; defocus blur; image scale; detection ... See more keywords

MA-GANet: A Multi-Attention Generative Adversarial Network for Defocus Blur Detection

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1 Published in 2022 at "IEEE Transactions on Image Processing"

DOI: 10.1109/tip.2022.3171424

Abstract: Background clutters pose challenges to defocus blur detection. Existing approaches often produce artifact predictions in background areas with clutter and relatively low confident predictions in boundary areas. In this work, we tackle the above issues… read more here.

Keywords: blur detection; defocus blur; generative adversarial; detection ... See more keywords

Learning local depth regression from defocus blur by soft-assignment encoding.

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1 Published in 2022 at "Applied optics"

DOI: 10.1364/ao.471105

Abstract: We present a novel, to the best of our knowledge, patch-based approach for depth regression from defocus blur. Most state-of-the-art methods for depth from defocus (DFD) use a patch classification approach among a set of… read more here.

Keywords: regression; depth; defocus blur; depth regression ... See more keywords