Showing 11 open source projects for "image"

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  • 1
    QualityScaler

    QualityScaler

    Image/video AI upscaler app (BSRGAN)

    Qualityscaler is a Windows app that uses BSRGAN Artificial Intelligence to enhance, enlarge and reduce noise in photographs and videos. QualityScaler is completely written in Python, from the backend to the front end. Image/list of images upscale. Video upscale. Drag&drop files [image / multiple images/video] Automatic image tiling and merging to avoid gpu VRAM limitation. Resize image/video before upscaling. Multiple Gpu support. Compatible images - png, jpeg, bmp, webp, tif. Compatible video - mp4, wemb, gif, mkv, flv, avi, mov, qt.
    Downloads: 125 This Week
    Last Update:
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  • 2
    Video2X

    Video2X

    A lossless video/GIF/image upscaler achieved with waifu2x, Anime4K

    A lossless video/GIF/image upscale achieved with waifu2x, Anime4K, SRMD and RealSR. Started in Hack the Valley 2, 2018. The latest Windows update is built based on version 4.8.1. GUI is not available for 5.0.0 yet, but is already under development. Go to the GUI page to see the basic usage of the GUI. Try the mirror if you can't download releases directly from GitHub.
    Downloads: 987 This Week
    Last Update:
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  • 3
    Real-ESRGAN GUI

    Real-ESRGAN GUI

    Cross-platform GUI for image upscaler Real-ESRGAN

    ...Real-ESRGAN can only enlarge the input image with a fixed 2-4x magnification (related to the selected model). This functionality is achieved by downsampling using a conventional scaling algorithm after multiple calls to Real-ESRGAN. Split each frame of the GIF and record the duration, zoom in one by one and then merge. Drag an image file or directory to any position in the window, and its path can be automatically set as the input.
    Downloads: 158 This Week
    Last Update:
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  • 4
    Anime Player

    Anime Player

    Video player for improving quality of hand-drawn images

    A video player that enhances the quality of a hand-drawn image using Anime4K's high-performance scaling algorithm. This program is a video player written in the Python programming language using the PySimpleGUI graphical user interface library, an mpv media player, and the Anime4K scaling algorithm . Anime Player is designed to play video and audio files and includes functions such as opening files, URLs and folders, setting image scaling parameters using the Anime4K algorithm, creating an mpv config for watching videos using the Anime4K algorithm on Android, viewing help and information about tuning the algorithm. ...
    Downloads: 13 This Week
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  • 5
    Warlock-Studio

    Warlock-Studio

    Suite with Real-ESRGAN, BSRGAN , RealESRNet, IRCNN, GFPGAN & RIFE.

    v5.1.1. 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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    Downloads: 21 This Week
    Last Update:
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  • 6
    AI Upscaler for Blender

    AI Upscaler for Blender

    AI Upscaler for Blender using Real-ESRGAN

    ...Upscaling is done entirely on the CPU. Blender renders a low-resolution image. The Real-ESRGAN Upscaler upscales the low-resolution image to a higher-resolution image. Real-ESRGAN is a deep learning upscaler that uses neural networks to achieve excellent results by adding in detail when it upscales.
    Downloads: 1 This Week
    Last Update:
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  • 7
    G2SConverter

    G2SConverter

    Convert models from GoldSource engine to Source engine with AI

    ...To improve the quality of the texture, it is first Upscaled using RealESRGAN. The user can select scaling factor: x2, x4 or x8. After the Upscaling procedure, the texture is deblured using the Scale-recurrent Network for Deep Image Deblurring. An example of a processed texture is shown in the following image (parameters used: scaling-factor = 4 and deblur iterations = 4) besides upscaling and debluring the utility also generates normal maps for each texture. This is implemented using the DeepBump by HugoTiny model. Examples of normal maps are shown in the following images.
    Downloads: 0 This Week
    Last Update:
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  • 8
    VSGAN

    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!
    Downloads: 1 This Week
    Last Update:
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  • 9
    Easy Upscale

    Easy Upscale

    A simple image upscaler application using EDSR, ESPCN, FSRCNN, etc.

    This application was made to fulfill the assignment for the Data Structures course. The concept of the application is an application to upgrade/enhance image quality. The main theme is queues, we implement circular queues for pooling/storing a list of images to be upscaled. Gui creation is made manually using the tkinter library. For the upscale process itself, it uses the OpenCV library with a model obtained from open source. Checked using vermin. Minimum required versions: 3.6 Incompatible versions: 2.
    Downloads: 0 This Week
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  • 10
    Upscale

    Upscale

    This program is upscaling any image by a factor 2 using an algorithm

    This program is upscaling any image by a factor 2 using an algorithm of cubic interpolation. You may need to install the following libraries to run the program, tqdm, itertools, and OpenCV.
    Downloads: 0 This Week
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  • 11
    Super-résolution via CNN

    Super-résolution via CNN

    Super resolution using a CNN, based on the work of the DGtal team

    ...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. Low res images should be located in a "dataset/input" folder, and high res targets in a "dataset/target" folder, where each different quality image has the same name in both folders.
    Downloads: 0 This Week
    Last Update:
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