Motion-blur kernel size estimation via learning a convolutional neural network
| Autor: | Nong Sang, Changxin Gao, Lerenhan Li, Luxin Yan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Deblurring
business.industry Motion blur ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering Pattern recognition 02 engineering and technology Convolutional neural network Kernel (image processing) Artificial Intelligence Variable kernel density estimation Kernel embedding of distributions Polynomial kernel Computer Science::Computer Vision and Pattern Recognition Signal Processing Radial basis function kernel 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Computer Vision and Pattern Recognition Artificial intelligence business Software Mathematics |
| Zdroj: | Pattern Recognition Letters. 119:86-93 |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2017.08.017 |
| Popis: | Deblurring is to restore a latent clear image as well as to estimate an underlying blur kernel from a single blurry image. Motion blur kernel size is a significant input parameter of existing deblurring algorithms. Setting the size manually, which is a tedious trial-and-error process, can hardly obtain a satisfactory result directly and effectively. In this paper, we formulate the kernel size estimation as a regression problem and construct a convolutional neural network (CNN) to resolve it. After training the CNN by a large number of simulated blurry images with labels, which are produced by a relative way, we can estimate the kernel sizes of a given blurry image without size limitation. Both quantitative and qualitative results of experiments show that our method can not only estimate the motion blur kernel size accurately but also help to yield better pictures with estimated kernel sizes as input parameters. |
| Databáze: | OpenAIRE |
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