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Techniques For Increasing Image Quality Without Buying a Better GPU
Single-Image Super-Resolution for Anime-Style Art using Deep Convolutional Neural Networks. And it supports photo.
Linux: https://github.com/nagadomi/waifu2x
Site (Has upload size limit): http://waifu2x.udp.jp/
Windows (Use Chrome's translate feature): http://inatsuka.com/extra/koroshell/
Waifu2x was designed to increase the quality of anime images and to re-size images. Thus depending on the style, the re-sizing and/or noise reducing will work to varying degrees.
IrfanView
"IrfanView has the best sharpening algorithm I've seen. After you resize your image with waifu, open it in irfanview and hit SHIFT+S to sharpen". - Neural-Style User on Reddit.
Works for Windows: http://www.irfanview.com/
NIN Upres
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content_image: VGG generated image
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style_image: original style (used to generate VGG version)
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keep the tv_weight as low as possible (I've used 0.00001)
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This technique works best for traditional-art styles (like van gogh's starry night) where you can see some grunge/noise. For smooth styles (anime/sharp-edged) you're better off using waifu2x.
Example of using NIN to increase Places205-VGG image size and quality:
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First, create an image with neural-style using the Places205-VGG model.
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Depending on the GPU resources available to you, either convert the previously created image to a .jpg or leave it as a png.
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Run the following command using the same style image that you originally used to create your content image. Make sure your content image is the one you created in step 1:
th neural_style.lua -style_image StyleImage.jpg -content_image ContentImage.jpg -output_image out.png -tv_weight 0.0001 -image_size 2500 -save_iter 50 -content_weight 10 -style_weight 1000 -num_iterations 1000 -model_file models/nin_imagenet_conv.caffemodel -proto_file models/train_val.prototxt -content_layers relu1,relu7,relu12 -style_layers relu1,relu3,relu5,relu7,relu9 -backend cudnn -cudnn_autotune -optimizer adam
Examples/Results:
Tubingen: https://imgur.com/a/ALzL7
Brad Pitt: https://imgur.com/a/Ws8x5
Escher Sphere: https://imgur.com/a/KS1mk
Stanford: https://imgur.com/a/M5jlz
Notes:
This has only been tested with Starry Night and the example images. I used an Amazon g2.2xlarge spot instance which had 4GB of GPU memory. This should work though with any combination of models and settings.
I used the following command to generate the original images:
th neural_style.lua -style_image StyleImage.jpg -content_image ContentImage.jpg -output_image out.png -tv_weight 0.0001 -save_iter 50 -num_iterations 1000 -model_file models/snapshot_iter_765280.caffemodel -proto_file models/deploy_10.prototxt -backend cudnn -cudnn_autotune -optimizer adam
Download the Places205-VGG model here: http://places.csail.mit.edu/model/places205vgg.tar.gz
Adobe InDesign Tiling
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Dream a neural-style result image
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In Adobe InDesign setup an overlapping grid and then paste the result image into each box.
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Set the document size to match your grid box size and make multiple pages for each box
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Output each of the pages as their own jpeg image using the export function.
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Dream each of the 12 images using the original style image. Best to setup a loop so you don't have to wait around running each one.
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Use the grid from step 2 and create a new document the exact size of the whole grid. Drag each of your new result images into each step of the grid and fit photo size to box.
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Use Gradient and Basic feather effects to blend the tile edges together
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Output final result image. Note - Since my Indesign document was setup at the size of the original result the final image resolution doesn't match the available resolution since each box now contains a higher resolution than the original 72dpi. To compensate I just up the resolution within the output settings and you gain detail in the final output.
Hopefully a tiling method that does not require InDesign is found/created soon.
Upres and Tiling Naming Conventions:
Model Name + Optional Tile Amount + Tiled + Optional Different Model Used for Upres + Upres
Examples:
VGG19 Tiled Upres - VGG19 was used for the orignal output and the Upres of each individual piece.
VGG19 Tiled NIN Upres - VGG19 was used for the orignal output and the NIN model was used for the Upres of each individual piece.
VGG19 4x3 Tiled Upres - VGG19 was used for the orignal output and the Upres of each individual piece. There were 4 tiles by 3 tiles used to create the final output.