Project Title: Artistic Style Transfer Using Deep Learning

Objective:

To transform an input image by applying the artistic style of another image (e.g., turning a photo into a painting in Van Gogh’s style), using deep learning techniques.

Concept:

Style Transfer blends:

Content Image: The main structure or objects.

Style Image: The color, texture, and patterns of an artwork.

Output: A new image preserving the content of the first image and the style of the second.

Key Steps:

Data Requirements:

No training data is required for basic style transfer (uses pre-trained models).

Content and style images provided by the user.

Model Architecture:

Based on Convolutional Neural Networks (CNNs), often using pre-trained VGG-19.

Uses:

Content Loss: Measures similarity between content image and output.

Style Loss: Compares Gram matrices of style image and output.

Total Variation Loss: Encourages spatial smoothness.

Approaches:

Optimization-Based Style Transfer: Iteratively updates the image to minimize total loss.

Fast Style Transfer: Trains a feed-forward network for real-time stylization.

Implementation Tools:

Python, NumPy

TensorFlow or PyTorch

OpenCV/Matplotlib for image processing

Output:

A visually striking image that artistically merges content and style.

Applications:

Artistic filters in mobile apps (e.g., Prisma)

Creative media and advertising

Real-time video stylization

Augmented reality effects