Super Resolution of the Partial Pixelated Images With Deep Convolutional Neural Network
| Autor: | Yun Fu, Yue Wu, Jun Li, Haiyi Mao |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Physics::Instrumentation and Detectors
Computer science business.industry Deep learning Resolution (electron density) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 010501 environmental sciences 01 natural sciences Superresolution Autoencoder Convolutional neural network Image (mathematics) Pixelation Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | ACM Multimedia |
| Popis: | The problem of super resolution of partial pixelated images is considered in this paper. Partial pixelated images are more and more common in nowadays due to public safety etc. However, in some special cases, for instance criminal investigation, some images are pixelated intentionally by criminals and partial pixelate make it hard to reconstruct images even a higher resolution images. Hence, a method is proposed to handle this problem based on the deep convolutional neural network, termed depixelate super resolution CNN(DSRCNN). Given the mathematical expression pixelates, we propose a model to reconstruct the image from the pixelation and map to a higher resolution by combining the adversarial autoencoder with two depixelate layers. This model is evaluated on standard public datasets in which images are pixelated randomly and compared to the state of arts methods, shows very exciting performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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