Search Results for "super resolution"
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Showing 19 open source projects for "super resolution"
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1
Waifu2x-Extension-GUI
Video, Image and GIF upscale/enlarge(Super-Resolution)
Photo/Video/GIF enlargement and Video frame interpolation using machine learning. Waifu2x-Extension-GUI is a video, image and GIF upscale/enlarge(Super-Resolution) and Video frame interpolation. Achieved with Waifu2x, Real-ESRGAN, Real-CUGAN, RTX Video Super Resolution VSR, SRMD, RealSR, Anime4K, RIFE, IFRNet, CAIN, DAIN, and ACNet. The beta build has a faster update cycle than the stable build, which allows you to experience the latest features of the software in advance. Beta builds are more unstable than the stable builds because the beta builds have not been fully tested before release. ... -
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AIOStreams
One addon to rule them all
AIOStreams is a “super-add-on” for Stremio that consolidates results from many add-ons and debrid services into a single, customizable feed. Instead of juggling multiple add-ons, you point AIOStreams at them; it then queries, merges, de-duplicates, and re-ranks everything according to your rules. The project includes a powerful filtering and sorting engine—think conditions on resolution, codec, provider, cached status, seed count, and more—so power users can shape results precisely. ... -
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NovaSR
A lightning fast audio upsampler
NovaSR is an extremely lightweight and high-performance audio upsampling model that transforms low-quality 16 kHz audio into clearer, high-fidelity 48 kHz audio with remarkable speed and efficiency. At only about 50 KB in size, the model is orders of magnitude smaller than typical audio super-resolution networks, yet it achieves high quality and realtime performance thanks to its compact architecture and efficient convolutional design. NovaSR is especially valuable for post-processing tasks in speech enhancement, TTS pipelines, and dataset restoration where low sampling rates degrade perceived audio clarity; the minimal model size also makes it suitable for edge and embedded use cases where memory is at a premium. ... -
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satellite-image-deep-learning
Resources for deep learning with satellite & aerial imagery
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... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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5
Smooth My Video
Offline AI frame interpolation, upscaling and HDR for NVIDIA GPUs
...Five interpolation models: GMFSS (best for anime), RIFE (best for live action) with a DRBA option that preserves anime pacing, NVIDIA DLSS 4.5 Frame Generation, Nvidia Smooth Motion, and SVP's svpflow engine. The same render can AI-upscale to 16K (RTX Video Super Resolution + Real-ESRGAN restore), sharpen, and convert SDR to real HDR10 with proper mastering metadata, plus optional Dolby Vision 8.1 and HDR10+ export. Original frames pass through untouched; output is 10-bit visually lossless HEVC, AV1 or H.266 with every audio track, subtitle, chapter and font preserved. Pause anytime or close the app - renders resume where they left off. ... -
6
Warlock-Studio
AI Suite for upscaling, interpolating & restoring images/videos
v6.0. Warlock-Studio is a Windows application that uses Real-ESRGAN, BSRGAN, IRCNN, GFPGAN, RealESRNet, RealESRAnime and RIFE Artificial Intelligence models to upscale, restore faces, interpolate frames and reduce noise in images and videos. the application supports GPU acceleration (including multi-GPU setups) and offers batch processing for large workloads. It includes drag-and-drop handling for single or multiple files, optional pre-resize functions, and an automatic tiling system... -
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Waifu2x-Extension-GUI
Photo/Video/GIF enlargement using machine learning
Image & GIF & Video Super-Resolution using Deep Convolutional Neural Networks. 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 -
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RenderPilot
Manage DLSS, FSR, XeSS and other PC game rendering libraries
RenderPilot is an open-source Windows application for managing rendering components used by PC games. It automatically discovers installed games and shows which rendering libraries they use, making it easy to update, downgrade, or restore components without manually working through game folders. It also integrates rendering add-ons such as RenoDX and Luma, provides per-game NVIDIA DLSS controls, and automatically keeps backups for safe rollback. Everything stays local: RenderPilot... -
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MMEditing
MMEditing is a low-level vision toolbox based on PyTorch
MMEditing is an open-source toolbox for low-level vision. It supports various tasks. MMEditing is a low-level vision toolbox based on PyTorch, supporting super-resolution, inpainting, matting, video interpolation, etc. We decompose the editing framework into different components and one can easily construct a customized editor framework by combining different modules. The toolbox directly supports popular and contemporary inpainting, matting, super-resolution and generation tasks. The toolbox provides state-of-the-art methods in inpainting/matting/super-resolution/generation. ... -
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10
enhancr
Video Frame Interpolation & Super Resolution using NVIDIA's TensorRT
enhancr is an elegant and easy to use GUI for Video Frame Interpolation and Video Upscaling which takes advantage of artificial intelligence - built using node.js and Electron. It was created to enhance the user experience for anyone interested in enhancing video footage using artificial intelligence. The GUI was designed to provide a stunning experience powered by state-of-the-art technologies without feeling clunky and outdated like other alternatives. It features blazing-fast TensorRT... -
11
PNG Upscale
AI Super - Resolution
Download is available from the Releases Page or Google Drive or MediaFire. Be careful when trying to resize very large pictures, it can take considerable time and resources. To upscale an image you just need to choose a mode, load a picture and press start. Save button can be used to choose an output folder and filename before you start the process (either just name or .png) You can double-click the text box to change [Dark - Light] theme (disabled when upScaling) Use PNG images for best... -
12
VSGAN
VapourSynth Single Image Super-Resolution Generative Adversarial
Single Image Super-Resolution Generative Adversarial Network (GAN) which uses the VapourSynth processing framework to handle input and output image data. Transform, Filter, or Enhance your input video, or the VSGAN result with VapourSynth, a Script-based NLE. You can chain models or re-run the model twice-over (or more). Have low VRAM? Don’t worry! -
13
compatible lite doom
Ms Dos port with slopes 3d floors true3d demo compatibility fast speed
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... -
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AviSynth AiUpscale v1.2.0
AviSynth+ implementation of Super-Resolution Convolutional Neural
...The low resolution images were generated using the bicubic filter with Catmull-Rom settings, which is the method commonly used for training super-resolution networks, including those tested here. Note however that as an exception to this, the Anime4K models were trained using the average area downsampling method. The AiUpscale models used for all datasets were the "Photo" models, except for the Manga109 dataset for which the "LineArt" models were used. -
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BasicSR
Winning Solution in NTIRE19 Challenges on Video Restoration
BasicSR is a deep learning framework designed for advanced video restoration tasks such as video super-resolution, deblurring, and denoising. Unlike single-image restoration models, EDVR addresses the temporal dimension by aligning multiple video frames using deformable convolutional layers in a coarse-to-fine manner, allowing it to effectively handle large motion and complex scene dynamics. The architecture includes bespoke modules (e.g., Pyramid, Cascading and Deformable alignment and Temporal Spatial Attention fusion) that align information across frames and emphasize important features for restoration, enabling state-of-the-art performance on benchmarks such as the REDS challenge. ... -
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GIMP ML
AI for GNU Image Manipulation Program
...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. -
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Super-résolution via CNN
Super resolution using a CNN, based on the work of the DGtal team
Super-resolution using a CNN, based on the work of the DGtal team. First of all, an Nvidia graphics card (neither AMD nor Intel integrated) is highly recommended to parallelize the CNN. You will then need to install CUDA. No CUDA = dozens of times slower. This program will generate "model_epoch_ .pth" files corresponding to the model at epoch n, in a folder saved_model_u t_bs bs_tbs tbs_lr lr, where corresponds to the scale factor, bsthe size of the training batch, tbsthe size of the test batch and lrto the learning rate. ... -
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Image Super-Resolution (ISR)
Super-scale your images and run experiments with Residual Dense
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. ... -
19
waifu2x
Single-image super-resolution for anime-style art
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.
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