Evidence Based Feature Selection and Collaborative Representation Towards Learning Based PSF Estimation for Motion Deblurring

Dhanakshirur, Rohan Raju; Tabib, Ramesh Ashok; Patil, Ujwala; Mudenagudi, Uma

doi:10.1109/iccvw.2019.00130

Cited by 2 publications

(3 citation statements)

References 34 publications

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“… The scene cannot be texture less and translational blur dominates. ★★★ Dhanakshirur [ 47 ] Markov Random Field Used for a dense non-uniform motion blur field. ★★★★ Variational Bayesian Shao et al [ 50 ] Variational Bayesian More adaptive sparse image prior with considerably less implementation heuristics.…”

Section: Edge Prediction Methodsmentioning

confidence: 99%

“…It is challenging to find distinct features to learn the extent of motion blur through deep learning precisionly. The Markov random field model was used to infer a dense non-uniform motion blur field enforcing motion smoothness in deep learning motion deblurring models [ 47 , 48 ]. Variational Bayesian method …”

Section: Development Of Image Motion Deblurring Techniquesmentioning

confidence: 99%

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A state-of-the-art review of image motion deblurring techniques in precision agriculture

Chen

2023

Heliyon

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Section: Edge Prediction Methodsmentioning

confidence: 99%

Section: Development Of Image Motion Deblurring Techniquesmentioning

confidence: 99%

A state-of-the-art review of image motion deblurring techniques in precision agriculture

Chen

2023

Heliyon

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“…The algorithm could finally restore the accurate infrared image [24][25][26]. With the development of image processing technology, more image deblurring algorithms have been proposed [27]. For example, Amudha Jeyaprakash et al generated low-rank matrices by extracting linearly uncorrelated principal components, and then combined the neural network model to implement image deblurring operation.…”

Section: Introductionmentioning

confidence: 99%

High‐speed target tracking algorithm for the pulse‐sequence‐based image sensor

Wang

Gao

et al. 2020

IET Image Processing

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This paper proposed a high-speed target tracking algorithm based on the pulse-sequencebased image sensor. The algorithm separates the target from the background through the statistical distribution of the pulses in a single frame of data. In order to achieve more accurate target tracking, the phenomenon of image lag in the process of capturing the high-speed moving target is studied. The positioning deviation is compensated by estimating the image lag length of the moving target. The algorithm is implemented with pipeline processing on field-programmable gate array (FPGA). The positioning accuracy of the algorithm is verified with input data from a pulse-sequence image sensor model. The verification results show that the maximum position deviation of the target at different speeds is 0.94 pixels. In the experiment of tracking a 40 m/s moving ball, this algorithm can provide real-time position information within 44 ns.

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Evidence Based Feature Selection and Collaborative Representation Towards Learning Based PSF Estimation for Motion Deblurring

Cited by 2 publications

References 34 publications

A state-of-the-art review of image motion deblurring techniques in precision agriculture

A state-of-the-art review of image motion deblurring techniques in precision agriculture

High‐speed target tracking algorithm for the pulse‐sequence‐based image sensor

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