Back to Search
Start Over
A Textural Layer L0 Gradient Minimization based De-noise Method for Scene Text Images
- Source :
- 2018 14th IEEE International Conference on Signal Processing (ICSP).
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- 978-1-5386-4673-1/18/$31.00 ©2018 IEEEText images in complex scenes are often suffer from various noises, which result in quality degradation and further effect the performance of automatic analysis. A novel denoise method for scene text images is proposed in this paper. With an image layered decomposition model, text images can be decomposed into a structural layer and a textual layer. Text component can be represented by the structural layer, and the texture layer of the image is closely related to the fine details and random noises. Based on this observation, a L 0 gradient minimization scheme is applied on the textual layer in our method. The L 0 gradient minimization is used to control the number of nonzero gradient values in a textured layer image, which maintains important features in the image while effectively suppress the gradients with small magnitude, which usually associate with noise. Comprehensive experimental results show that the proposed method can effectively remove the noise in the images and improve the visual quality of the text images.
- Subjects :
- Noise measurement
Computer science
business.industry
Low-pass filter
Noise reduction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
020207 software engineering
Pattern recognition
02 engineering and technology
Texture (music)
Image (mathematics)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Noise (video)
Artificial intelligence
Minification
Layer (object-oriented design)
business
Subjects
Details
- ISBN :
- 978-1-5386-4673-1
- ISBNs :
- 9781538646731
- Database :
- OpenAIRE
- Journal :
- 2018 14th IEEE International Conference on Signal Processing (ICSP)
- Accession number :
- edsair.doi...........cbf3731e1e9a08b001bbf0931f734386
- Full Text :
- https://doi.org/10.1109/icsp.2018.8652439