8 research outputs found
The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution
While implicit generative models such as GANs have shown impressive results
in high quality image reconstruction and manipulation using a combination of
various losses, we consider a simpler approach leading to surprisingly strong
results. We show that texture loss alone allows the generation of perceptually
high quality images. We provide a better understanding of texture constraining
mechanism and develop a novel semantically guided texture constraining method
for further improvement. Using a recently developed perceptual metric employing
"deep features" and termed LPIPS, the method obtains state-of-the-art results.
Moreover, we show that a texture representation of those deep features better
capture the perceptual quality of an image than the original deep features.
Using texture information, off-the-shelf deep classification networks (without
training) perform as well as the best performing (tuned and calibrated) LPIPS
metrics. The code is publicly available.Comment: 19 pages, 14 figure
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Gaussian Process Modeling for Upsampling Algorithms With Applications in Computer Vision and Computational Fluid Dynamics
Across a variety of fields, interpolation algorithms have been used to upsample lowresolution or coarse data fields. In this work, novel Gaussian Process based methodsare employed to solve a variety of upsampling problems. Specifically threeapplications are explored: coarse data prolongation in Adaptive Mesh Refinement(AMR) in the field of Computational Fluid Dynamics, accurate document imageupsampling to enhance Optical Character Recognition (OCR) accuracy, and fastand accurate Single Image Super Resolution (SISR). For AMR, a new, efficient,and “3rd order accurate” algorithm called GP-AMR is presented. Next, a novel,non-zero mean, windowed GP model is generated to upsample low resolution documentimages to generate a higher OCR accuracy, when compared to the industrystandard. Finally, a hybrid GP convolutional neural network algorithm is used togenerate a computationally efficient and high quality SISR model