Image Contrast Enhancment Using Feed Forward Network
| Autor: | Sudhir Kumar Rathi, Manish Bhamu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Brightness
Artificial neural network Computer science business.industry Image quality media_common.quotation_subject 0206 medical engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology 020601 biomedical engineering Peak signal-to-noise ratio Image (mathematics) ComputingMethodologies_PATTERNRECOGNITION Computer Science::Computer Vision and Pattern Recognition Computer Science::Multimedia 0202 electrical engineering electronic engineering information engineering Unsupervised learning Contrast (vision) 020201 artificial intelligence & image processing Artificial intelligence business Histogram equalization media_common |
| Zdroj: | International Journal Of Engineering And Computer Science. |
| ISSN: | 2319-7242 |
| Popis: | Histogram Equalization (HE) is a popular, simple, fast and effective technique for improving the gray image quality. Contrast enhancement was very popular method but it was not able to preserve the brightness of image. Image Dependent Brightness Preserving Histogram Equalization (IDBPHE) technique improve the contrast as well as preserve the brightness of a gray image. Image features Peak Signal to Noise Ratio (PSNR) and Absolute Mean Brightness Error (AMBE) are the parameters to measure the improvement in a gray image after applying the algorithm. Unsupervised learning algorithm is an important method to extract the features of neural network. We propose an algorithm in which we extract the features of an image by unsupervised learning. After apply unsupervised algorithm on the image the PSNR and AMBE features are improved. |
| Databáze: | OpenAIRE |
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