DeepImageBlending
Image Blender
A PyTorch implementation of blending images by optimizing a Poisson loss with style and content loss
This is a Pytorch implementation of deep image blending
435 stars
14 watching
77 forks
Language: Python
last commit: about 1 year ago Related projects:
Repository | Description | Stars |
---|---|---|
| Deep learning implementation of image matting, aiming to separate foreground and background from input images. | 293 |
| A PyTorch project for comparing image classification models and facilitating quick experiment setup | 366 |
| An implementation of cascaded refinement networks for generating photorealistic images from semantic layouts | 65 |
| An implementation of deep image matting in PyTorch using a neural network architecture. | 821 |
| Implementation of a deep learning model for generating high-quality images with improved stability and variation. | 538 |
| An implementation of an image-to-image translation algorithm using deep learning and PyTorch | 428 |
| A Python framework for building deep learning models with optimized encoding layers and batch normalization. | 2,044 |
| A PyTorch toolbox for supporting research and development of domain adaptation, generalization, and semi-supervised learning methods in computer vision. | 1,236 |
| A deep learning-based method to improve image quality by reducing blur effects | 839 |
| An implementation of the DeepDream algorithm in PyTorch to generate visually striking images from input images. | 102 |
| A PyTorch implementation of an image-to-image translation model that generates new images from paired training data. | 1,491 |
| An implementation of Interactive Deep Colorization in PyTorch, providing a framework for real-time user-guided image colorization. | 600 |
| An implementation of a deep learning model for image segmentation using PyTorch | 868 |
| An implementation of DeepDream algorithm using PyTorch for image processing and computer vision. | 133 |
| A PyTorch implementation of a deep learning model for semantic image segmentation with annotated object parts. | 46 |