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1

Waifu2x-Extension-GUI

Photo/Video/GIF enlargement using machine learning

...Built-in image processing algorithm: Waifu2x / SRMD / RealSR / Anime4K / ACNet Built-in image processing engine: Waifu2x-caffe / Waifu2x-converter / Waifu2x-ncnn-vulkan / SRMD-ncnn-vulkan / RealSR-ncnn-vulkan / Anime4KCPP Github: https://github.com/AaronFeng753/Waifu2x-Extension-GUI

Downloads: 653 This Week

Last Update: 3 days ago
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2

waifu2x-cpu-torch-vks

waifu2x fork without CUDA

The original is here: https://github.com/nagadomi/waifu2x Differences: - CPU only, no CUDA needed; - double-precision version; - GPU and CPU-trained models (on the fly conversion in memory); - adapted for Lua v5.2 (works with Torch 7 on x32 Ubuntu). 31-12-2016: - removed unneeded data copies, left from CUDA processing; - removed large duplicate files (see "ReadMe" before use).

Downloads: 1 This Week

Last Update: 2016-12-31
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3

waifu2x-ROS

ReactOS and Windows XP compatible build of waifu2x.

To speed up, use "-b 256 -j 4" options. The original is there: https://github.com/WL-Amigo/waifu2x-converter-cpp Here are some minor cosmetic changes (switch to control memory usage and slightly reformatted output) and files needed to build under MinGW32-W64. To recompile (in Linux), run "make clean; make". 2016-10-17 UPDATE: added simple GUI. 2016-10-18 FIX: corrected typo, added some crashes handling. 2016-11-20 UPDATE: - added "TTA" and "number of passes" options; - enabled RGB models (without internal upscaling); - updated JPEG, TIFF, WebP and OpenEXR libraries; - modified OpenCV to enable JPEG compression in TIFF images (sources included); - replaced models with Nagadomi's ones (https://github.com/nagadomi/waifu2x). 2016-12-28: Happy New Year and Merry Christmas! ...

Downloads: 0 This Week

Last Update: 2016-12-28
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4

blitz-image

GPU-accelerated image processing for java

...The performance of a typical bicubic upscale operation is typically in the order of 10 times faster than via the standard AWT mechanism using RenderingHints.VALUE_INTERPOLATION_BICUBIC. The library depends on Aparapi (https://code.google.com/p/aparapi/). Any GPU supporting OpenCL should be supported (including low-end GPUs without double-precision fp support), and there is automatic fallback to pure java implementations for all operations in the event that there is no OpenCL support available. A high-level API allows for very easy use from AWT/Swing, whilst the low-level array-based API allows integration with any imaging framework (e.g. from Android or SWT).

Downloads: 0 This Week

Last Update: 2016-06-15
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