Search Results for "super resolution"

This page lists resources for performing deep learning on satellite imagery. To a lesser extent classical Machine learning (e.g. random forests) are also discussed, as are classical image processing techniques. Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research and developer communities. If you find this work useful...

Dos Doom with 3d floors, slopes, destructible architecture, demo compatibility 1.9, fast speed youtube https://goo.gl/pbNybV Requirements: 486DX, 4MB RAM Win95, 8MB Dos(run -ram8mb parm) https://goo.gl/agT2wc https://goo.gl/3EFVBg Recommended: 486DX4 100MHz, 16MB RAM https://goo.gl/IWGLkQ Run on Dos,Win9x, XP 7 32 bits, Dosbox Turbo Android requires Dosbox: Win 64bit or WinXP w/ some video drivers https://goo.gl/FE6rRF Bugs: command line needs one space after wad or other parm to...

...It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on images such as edge detection and color clustering have also been added. GIMP-ML relies on standard Python packages such as numpy, scikit-image, pillow, pytorch, open-cv, scipy. In addition, GIMP-ML also aims to bring the benefits of using deep learning networks used for computer vision tasks to routine image processing workflows.

The goal of this project is to upscale and improve the quality of low-resolution images. This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components. Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few commands. ...

Single-Image Super-Resolution for Anime-Style Art using Deep Convolutional Neural Networks. And it supports photo. You can train your own model, change image size, reduce image noise, upscale and customize your image's style. It provides the option of converting and downloading your edited images.