Search Results for "python neural network"

Blender add-on to dramatically reduce render times using the Real-ESRGAN upscaler. Rendering an HD image in Blender takes 37 minutes. Upscaling can render a similar quality image in 5 mins total. Any PC or laptop can now do 3D rendering. 4k images can be rendered in the time it would take to render HD 1080p images. HD 1080p images can be rendered in record time on low-end hardware. Installation is easy. Just install the addon. No special hardware or GPU is required. Upscaling is done...

Sharpen your low-resolution pictures with the power of AI upscaling. SuperImage is a neural network-based image upscaling build with the MNN deep learning framework and the Real-ESRGAN algorithm. By leveraging the power of your device's GPU, SuperImage is able to upscale and restore the details of your images without uploading them to the internet, keeping your data secure. SuperImage is a neural network-based image upscaling application for Android built with the MNN deep learning framework and Real-ESRGAN. ...

...A feature of this utility is the ability to improve the quality of textures of models using Upscaling, deblurring, and normal map generating. All operations to improve the quality of textures are performed by neural networks. 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. ...

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! The Network will be applied in quadrants of the image to reduce up-front VRAM usage. You can use any RGB video input, including float32 (e.g.,...

We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance. Our method is much lighter than previous approaches and can process 4K at 76 FPS and HD at 104 FPS on an Nvidia GTX 1080Ti GPU. Unlike most existing methods that perform video matting frame-by-frame as independent images, our method uses a recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and...