18734000. Learning-Based Lens Flare Removal simplified abstract (Google LLC)
Contents
Learning-Based Lens Flare Removal
Organization Name
Inventor(s)
Qiurui He of Mountain View CA (US)
Tianfan Xue of Sunnyvale CA (US)
Rahul Garg of Sunnyvale CA (US)
Jiawen Chen of San Ramon CA (US)
Jonathan T. Barron of Alameda CA (US)
Learning-Based Lens Flare Removal - A simplified explanation of the abstract
This abstract first appeared for US patent application 18734000 titled 'Learning-Based Lens Flare Removal
The method described in the abstract involves using a machine learning model to remove lens flare from an input image, resulting in a de-flared image.
- The machine learning model is trained using training images that combine baseline images with corresponding lens flare images.
- The model adjusts its parameters based on the loss value determined by comparing the modified image to the corresponding baseline image.
- By processing the input image through the machine learning model, at least part of the lens flare representation is removed.
Potential Applications: - Image editing software to automatically remove lens flare from photos. - Enhancing the quality of images taken in bright light conditions.
Problems Solved: - Eliminates the need for manual removal of lens flare from images. - Improves the overall quality of images by reducing unwanted lens flare effects.
Benefits: - Saves time and effort in post-processing images. - Enhances the visual appeal of photos by removing distracting lens flare.
Commercial Applications: Title: Automated Lens Flare Removal Technology for Image Editing Software This technology can be utilized in photography software applications to provide users with a convenient tool for removing lens flare from their photos. This can appeal to professional photographers, hobbyists, and anyone looking to enhance the quality of their images.
Questions about Lens Flare Removal Technology: 1. How does the machine learning model differentiate between lens flare and other elements in the image? The machine learning model is trained on a dataset that includes both baseline images and images with lens flare, allowing it to learn the characteristics of lens flare and distinguish it from other elements in the image.
2. Can this technology be applied to videos as well, or is it limited to still images? The technology can potentially be adapted for video processing as well, as long as the machine learning model is optimized for real-time performance and can handle the additional complexity of video frames.
Original Abstract Submitted
A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.
