Deep Learning based Radial Blur Estimation and Image Enhancement
Autor: | Ujwala Patil, Vaishnavi Hurakadli, Sujaykumar Kulkarni, Ramesh Ashok Tabib, Uma Mudengudi |
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Rok vydání: | 2019 |
Předmět: |
0209 industrial biotechnology
Deblurring business.industry Computer science Deep learning Pipeline (computing) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Image enhancement Real image Two stages Autoencoder Image (mathematics) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business ComputingMethodologies_COMPUTERGRAPHICS |
Zdroj: | 2019 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). |
Popis: | In this paper, we propose a deep learning based pipeline to estimate the radial blur and enhance the deblurred image. The radial blur is introduced in the image as an effect of ego motion in autonomous vehicle systems. The deblurring of the image with radial blur is challenging since most of the blur models do not estimate radial blur. Hence, we design a deep learning based pipeline with estimation and enhancement modules. The estimation module is designed with CuratorNet to estimate radial PSF in two stages. The estimated PSF is used for deblurring of input radial blurred images. The enhancement module is designed with convolutional autoencoder which enhances the deblurred image to remove artefacts in order to detect the traffic signs. We demonstrate the results of the proposed pipeline on synthetic and real images with traffic signs and compare the results with existing methods. |
Databáze: | OpenAIRE |
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