📸OLD PHOTO RESTORATION via DEEP LATENT SPACE TRANSLATION👩💻👨💻
Old or Vintage photo restoration is an important field of computer vision. We all want the old pictures of our loved ones to be clear and beautiful.A picture like this doesn’t honor the person nor brings back as many good memories as a better-looking picture like this one does.We all have at least one old photo that we care about and is looking worse and worse with time. This is why many are working to solve this issue using various technologies and techniques.
INTRODUCTION
Old photo restoration via deep latent space translation is a technique used to restore old, damaged, or low-quality photos by using deep learning models. The goal of this technique is to improve the visual quality of old photos, which may have deteriorated over time due to factors such as fading, scratches, dust and various reasons as per the passing period.This technique involves training a deep learning model on a large dataset of paired images, where one image is a degraded version of the other. The model learns to map the degraded images to their corresponding high-quality versions by using a technique known as latent space translation.
WHAT IS DEEP LATENT SPACE TRANSLATION ?
Deep latent space translation is a technique in deep learning that involves mapping data from one space, known as the input space, to another space, known as the latent space, and then transforming the data in the latent space to produce output data in a different space, known as the output space. This technique is commonly used for image processing tasks, such as image-to-image translation, style transfer, and image super-resolution.
In latent space translation, the deep learning model maps the input image to a lower-dimensional space known as the latent space. This space represents the underlying features of the image, such as its color, texture, and shape. The model then applies a transformation to the latent space to obtain a new set of features that represent the restored image. Finally, the restored image is generated from these new features.
The advantage of using deep latent space translation for old photo restoration is that it can handle a wide range of image degradation types and can produce high-quality restored images that are visually similar to the original photos. This technique has many potential applications in fields such as art preservation, historical document restoration, and genealogy research.
HOW IT IS ACHIEVED ?
Old photo restoration via deep latent space translation involves the following steps:
Collect a dataset of paired images:-
The first step is to collect a dataset of paired images, where one image is a degraded or low-quality version of the other. The degraded images may have faded colors, scratches, dust, or other types of damage. The high-quality images should be visually similar to the degraded images, but with higher resolution and better visual quality.
Train a deep learning model:-
The next step is to train a deep learning model on the dataset of paired images. The model should be designed to map the degraded images to their corresponding high-quality versions by using deep latent space translation. The training process involves optimizing the model's weights and biases to minimize the difference between the model's predictions and the ground truth high-quality images.
Map the degraded images to the latent space:-
Once the deep learning model is trained, the degraded images can be mapped to the latent space by using the encoder network. The encoder network takes the degraded image as input and produces a latent code, which represents the underlying features of the image in the latent space.
Transform the latent code:-
The next step is to transform the latent code to produce a new set of features that represent the restored image. This is achieved by applying a transformation to the latent code, which can be learned by the deep learning model during training. The transformation should be designed to produce a high-quality restored image that is visually similar to the corresponding high-quality image in the dataset.
Generate the restored image:-
Finally, the restored image is generated by using the decoder network to map the transformed latent code to the output space. The decoder network takes the transformed latent code as input and produces the restored image as output. The restored image should be visually similar to the corresponding high-quality image in the dataset and should show significant improvements over the degraded input image.
For a detailed outlook for this process,I have hereby attached video link.
Overall, the process of old photo restoration via deep latent space translation involves training a deep learning model to map degraded images to their corresponding high-quality versions by using a combination of latent space mapping, transformation, and image generation. By applying this technique, it is possible to restore old and damaged photos and improve their visual quality.