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Efficiency of texture image filtering and its prediction
- Source :
- Signal, Image and Video Processing, Signal, Image and Video Processing, Springer Verlag, 2016, 10 (8), pp.1543--1550. ⟨10.1007/s11760-016-0969-3⟩, Signal, Image and Video Processing, 2016, 10 (8), pp.1543--1550. ⟨10.1007/s11760-016-0969-3⟩
- Publication Year :
- 2016
- Publisher :
- HAL CCSD, 2016.
-
Abstract
- International audience; Textures are typical elements of natural scene images widely used in pattern recognition and image classification. Noise, often being present in acquired images, deteriorates texture features (characteristics), and it is desirable both to suppress it and to preserve a texture. This task is quite difficult even for the most advanced filters, and the resulting denoising efficiency can be quite low. Due to this, it is desirable to predict a denoising efficiency before filtering to decide whether it is worth filtering a given image or not. In this paper, we analyze several quantitative criteria (metrics) that can characterize filtering efficiency. Prediction strategy is described and its accuracy is studied. Several modern filtering techniques are analyzed and compared. Based on this, practical recommendations are given. © 2016, Springer-Verlag London.
- Subjects :
- Practical recommendation
Computer science
Image classification
Noise reduction
Noise suppression
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
Efficiency
Texture (music)
Filtering technique
Visual qualities
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]
Image texture
Texture filtering
Pattern recognition
Natural scene images
0202 electrical engineering, electronic engineering, information engineering
Median filter
Computer vision
Electrical and Electronic Engineering
Texture features
Contextual image classification
business.industry
020206 networking & telecommunications
Filtering efficiency
[SPI.TRON]Engineering Sciences [physics]/Electronics
Quantitative criteria
Signal Processing
Pattern recognition (psychology)
Image enhancement
020201 artificial intelligence & image processing
Noise (video)
Artificial intelligence
business
Forecasting
Subjects
Details
- Language :
- English
- ISSN :
- 18631703 and 18631711
- Database :
- OpenAIRE
- Journal :
- Signal, Image and Video Processing, Signal, Image and Video Processing, Springer Verlag, 2016, 10 (8), pp.1543--1550. ⟨10.1007/s11760-016-0969-3⟩, Signal, Image and Video Processing, 2016, 10 (8), pp.1543--1550. ⟨10.1007/s11760-016-0969-3⟩
- Accession number :
- edsair.doi.dedup.....d42b1fe76f3fdf92497d68fa0d01d54c