18155420. Image Enhancement via Iterative Refinement based on Machine Learning Models simplified abstract (Google LLC)
Image Enhancement via Iterative Refinement based on Machine Learning Models
Organization Name
Inventor(s)
Chitwan Saharia of Toronto (CA)
Jonathan Ho of Berkeley CA (US)
Mohammad Norouzi of Toronto (CA)
Image Enhancement via Iterative Refinement based on Machine Learning Models - A simplified explanation of the abstract
This abstract first appeared for US patent application 18155420 titled 'Image Enhancement via Iterative Refinement based on Machine Learning Models
Simplified Explanation
The patent application describes a method for training a neural network to enhance images by applying a forward Gaussian diffusion process. Here is a simplified explanation of the abstract:
- The method involves using a computing device to receive training data consisting of pairs of images, where each pair includes an image and a corresponding target version of the image.
- A neural network is trained based on this data to predict an enhanced version of an input image.
- The training process includes applying a forward Gaussian diffusion process, which adds Gaussian noise to the target versions of the images.
- This noise enables iterative denoising of the input image, using a reverse Markov chain associated with the forward Gaussian diffusion process.
- The trained neural network is then outputted.
Potential Applications
This technology has potential applications in various fields, including:
- Image enhancement: The trained neural network can be used to enhance the quality of images, making them clearer and more visually appealing.
- Medical imaging: The method can be applied to improve the quality of medical images, aiding in accurate diagnosis and treatment planning.
- Surveillance and security: Enhancing low-quality surveillance footage can help in identifying individuals and objects more effectively.
- Photography: The method can be used to enhance photographs, improving their overall quality and detail.
Problems Solved
The method addresses the following problems:
- Image noise: By applying a forward Gaussian diffusion process and iterative denoising, the method effectively reduces noise in images, resulting in clearer and more accurate representations.
- Image enhancement complexity: The neural network training process simplifies the task of enhancing images, automating the process and reducing the need for manual editing.
- Low-quality images: The method can enhance low-quality images, making them more usable and visually appealing.
Benefits
The technology offers several benefits:
- Improved image quality: The trained neural network can enhance images, improving their overall quality, sharpness, and detail.
- Automation: The method automates the image enhancement process, reducing the need for manual editing and saving time.
- Versatility: The trained neural network can be applied to various types of images, making it a versatile solution for image enhancement tasks.
- Accuracy: By using a reverse Markov chain and iterative denoising, the method achieves accurate and reliable image enhancement results.
Original Abstract Submitted
A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.
