Tasks

Image-to-Image

Image to image is the task of transforming a source image to match the characteristics of a target image or a target image domain. Any image manipulation and enhancement is possible with image to image models.

Inputs
Image-to-Image Model
Output

About Image-to-Image

Use Cases

Style transfer

One of the most popular use cases of image to image is the style transfer. Style transfer models can convert a regular photography into a painting in the style of a famous painter.

Task Variants

Image inpainting

Image inpainting is widely used during photography editing to remove unwanted objects, such as poles, wires or sensor dust.

Image colorization

Old, black and white images can be brought up to life using an image colorization model.

Super Resolution

Super resolution models increase the resolution of an image, allowing for higher quality viewing and printing.

Inference

You can add a small snippet here that shows how to infer with image-to-image models.

Useful Resources

You can contribute useful resources about this task here.

Most Used Model for the Task

Pix2Pix is a popular model used for image to image translation tasks. It is based on a conditional-GAN (generative adversarial network) where instead of a noise vector a 2D image is given as input. More information about Pix2Pix can be retrieved from this link where the associated paper and the GitHub repository can be found.

Below images show some of the examples shared in the paper that can be obtained using Pix2Pix. There are various cases this model can be applied on. It is capable of relatively simpler things, e.g. converting a grayscale image to its colored version. But more importantly, it can generate realistic pictures from rough sketches (can be seen in the purse example) or from painting-like images (can be seen in the street and facade examples below).

Alt text

References

[1] P. Isola, J. -Y. Zhu, T. Zhou and A. A. Efros, "Image-to-Image Translation with Conditional Adversarial Networks," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 5967-5976, doi: 10.1109/CVPR.2017.632.

This page was made possible thanks to the efforts of Paul Gafton and Osman Alenbey.

Image-to-Image demo

No example widget is defined for this task.

Note Contribute by proposing a widget for this task !

Models for Image-to-Image
Browse Models (35)

Note A model that enhances images captured in low light conditions.

Note A model that increases the resolution of an image.

Note A model that creates a set of variations of the imput image in the style of DALL-E using Stable Diffusion.

Datasets for Image-to-Image

Note Multiple images of celebrities, used for facial expression translation

Metrics for Image-to-Image
PSNR
Peak Signal to Noise Ratio (PSNR) is an approximation of the human perception, considering the ratio of the absolute intensity with respect to the variations. Measured in dB, a high value indicates a high fidelity.
SSIM
Structural Similarity Index (SSIM) is a perceptual metric which compares the luminance, contrast and structure of two images. The values of SSIM range between -1 and 1, and higher values indicate closer resemblance to the original image.
IS
Inception Score (IS) is an analysis of the labels predicted by an image classification model when presented with a sample of the generated images.