Fast Image Processing with Fully-Convolutional Networks (1709.00643v1)

Abstract: We present an approach to accelerating a wide variety of image processing operators. Our approach uses a fully-convolutional network that is trained on input-output pairs that demonstrate the operator's action. After training, the original operator need not be run at all. The trained network operates at full resolution and runs in constant time. We investigate the effect of network architecture on approximation accuracy, runtime, and memory footprint, and identify a specific architecture that balances these considerations. We evaluate the presented approach on ten advanced image processing operators, including multiple variational models, multiscale tone and detail manipulation, photographic style transfer, nonlocal dehazing, and nonphotorealistic stylization. All operators are approximated by the same model. Experiments demonstrate that the presented approach is significantly more accurate than prior approximation schemes. It increases approximation accuracy as measured by PSNR across the evaluated operators by 8.5 dB on the MIT-Adobe dataset (from 27.5 to 36 dB) and reduces DSSIM by a multiplicative factor of 3 compared to the most accurate prior approximation scheme, while being the fastest. We show that our models generalize across datasets and across resolutions, and investigate a number of extensions of the presented approach. The results are shown in the supplementary video at https://youtu.be/eQyfHgLx8Dc

• Fully-Convolutional Networks (FCNs) use only convolutional layers to efficiently process images of any size for dense prediction tasks like semantic segmentation.
• FCNs enable high-speed image processing by replacing computationally expensive fully connected layers, making them suitable for real-time computer vision applications.
• Their end-to-end processing approach allows for faster inference compared to patch-based methods, which is crucial for efficient image analysis.

I'm sorry, but it appears you have provided a LaTeX document reference or a command that includes a PDF file which, unfortunately, I cannot review. Therefore, I'm unable to write an essay about the paper without access to its content. If you could provide the paper's text or summarize its main points, I would be happy to help write an essay based on that information.