Full-Scene Defocus Blur Detection With DeFBD+ via Multi-Level Distillation Learning

Zhao, Wenda; Wei, Fei; Wang, Haipeng; He, You; Lu, Huchuan

doi:10.1109/tmm.2023.3248162

Cited by 7 publications

(1 citation statement)

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“…Their model efficiently detected the blur-map from the source defocused-blur image. Likewise, Wenda et al [51] proposed a set of separate and combined models, i.e., pixel level DBD network and an image level DBD classification network, to accomplish accurate results for various defocus-blur images. Their proposed study was evaluated using their own DBD dataset called DeFBD+, along with annotations at pixel level and outperformed.…”

Section: Literature Review Presentlymentioning

confidence: 99%

A Novel Defocus-Blur Region Detection Approach Based on DCT Feature and PCNN Structure

Basar,

Ali,

Waheed

et al. 2023

IEEE Access

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The motion or out-of-focus effect in digital images is the main reason for the blurred regions in defocused-blurred images. It may adversely affect various image features such as texture, pixel, and region. Therefore, it is important to detect in-focused objects in defocused-blurred images after the segmentation of blurred and non-blurred regions. The state-of-the-art techniques are prone to noisy pixels, and their local descriptors for developing segmentation metrics are also complex. To address these issues, this research, therefore, proposed a novel and hybrid focused detection approach based on Discrete Cosine Transform (DCT) coefficients and PC Neural Net (PCNN) structure. The proposed approach partially resolves the limitations of the existing contrast schemes to detect focused smooth objects from the outof-focused smooth regions in the defocus dataset. The visual and quantitative evaluation illustrates that the proposed approach outperformed in terms of accuracy and efficiency to referenced algorithms. The highest ℱ 𝛼 -score of the proposed approach on Zhao's dataset is 0.7940 whereas on Shi's dataset is 0.9178.

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Section: Literature Review Presentlymentioning

confidence: 99%