Deep Photo Styletransfer | Awesome Repository

This project is a deep learning style transfer framework designed to apply artistic styles to photographs. It functions as a photorealistic image stylizer that merges the content of one image with the visual characteristics of another while maintaining the original geometry and structural details.

The system distinguishes itself through the use of matting Laplacian matrices and semantic segmentation masks to prevent distortion and preserve edge fidelity. These capabilities allow for region-specific styling, where different aesthetics can be applied to distinct objects or areas within a single image based on color-coded labels.

The framework covers neural image synthesis and artistic image processing, utilizing feature reconstruction and regularization to ensure that the resulting stylized versions maintain the integrity of the original scene.

Features

Neural Style Transfer - Provides a framework for transferring artistic styles to photographs while preserving original photorealistic structures.
CNN Image Stylizers - Uses convolutional neural networks to transform photographs into stylized versions that maintain scene integrity.
Segmented Style Application - Applies distinct artistic styles to specific image regions guided by segmentation masks.
Matting Laplacian Regularization - Employs matting Laplacian regularization to maintain photorealistic structures and edge fidelity during style transfer.

Features

Neural Style Transfer - Provides a framework for transferring artistic styles to photographs while preserving original photorealistic structures.
CNN Image Stylizers - Uses convolutional neural networks to transform photographs into stylized versions that maintain scene integrity.
Segmented Style Application - Applies distinct artistic styles to specific image regions guided by segmentation masks.
Matting Laplacian Regularization - Employs matting Laplacian regularization to maintain photorealistic structures and edge fidelity during style transfer.