Introduction

Artificial Intelligence has revolutionized the world of image processing. Techniques like Neural Style Transfer and Image Inpainting have made it possible to generate images and transform them into various art styles. The basic idea behind both techniques is similar to that of deep learning architectures that rely on a neural network to learn different features of images. However, the difference lies in their application.

Neural Style Transfer

Neural Style Transfer, as the name suggests, involves applying a particular artistic style to an input image. The style of the image is transferred onto another image that is called the content image. The result is a new image that has the content of one image and the style of another.

The example below shows an original image and its stylized versions using different art styles.

The Neural Style Transfer method involves using pre-trained networks to encode the content and style of an image. The content is encoded using the convolutional layers of the network, which capture features like edges and textures. The style, on the other hand, is encoded using statistical properties of the images, like correlations and variances.

Image Inpainting

Image Inpainting, as the name suggests, involves filling in the missing parts of an image. The technique is widely used in image editing and restoration. It involves using deep learning algorithms to generate new content that matches the context of the surrounding pixels.

The example above shows an original image and the same image with a masked area filled in using Image Inpainting.

The algorithm works by first encoding the image using a neural network. The image is then reconstructed by copying the surrounding pixels and filling in the missing parts using data from the encoded image.

Comparing Neural Style Transfer and Image Inpainting

While both techniques use similar approaches to neural networks, they have different applications. Neural Style Transfer is used to create stylized images while Image Inpainting is used to restore and edit images.

In terms of processing compatibility, Neural Style Transfer requires more computing resources due to the need for training multiple models for different styles. On the other hand, Image Inpainting requires fewer resources as it involves generating new content from the pre-existing data.

Conclusion

Neural Style Transfer and Image Inpainting are both powerful tools in image processing. While they have similar neural architecture, they have different applications. Neural Style Transfer is used for stylization, while Image Inpainting is used for image restoration and editing. Both techniques have their advantages and drawbacks, and each is suitable for a particular type of use case.

References

• Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576.
• Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016). Context encoders: Feature learning by inpainting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2536-2544).