Generic 3D Convolutional Fusion for Image Restoration
| Autor: | Jiqing Wu, Radu Timofte, Luc Van Gool |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Network architecture
business.industry Computer science Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering Pattern recognition 02 engineering and technology Convolutional neural network Image (mathematics) Stochastic gradient descent Complementarity (molecular biology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Image restoration Feature detection (computer vision) |
| Zdroj: | Computer Vision – ACCV 2016 Workshops ISBN: 9783319544069 ACCV Workshops (1) |
| DOI: | 10.1007/978-3-319-54407-6_11 |
| Popis: | Also recently, exciting strides forward have been made in the area of image restoration, particularly for image denoising and single image super-resolution. Deep learning techniques contributed to this significantly. The top methods differ in their formulations and assumptions, so even if their average performance may be similar, some work better on certain image types and image regions than others. This complementarity motivated us to propose a novel 3D convolutional fusion (3DCF) method. Unlike other methods adapted to different tasks, our method uses the exact same convolutional network architecture to address both image denoising and single image super-resolution. Our 3DCF method achieves substantial improvements (0.1 dB–0.4 dB PSNR) over the state-of-the-art methods that it fuses on standard benchmarks for both tasks. At the same time, the method still is computationally efficient. |
| Databáze: | OpenAIRE |
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