Samsung electronics co., ltd. (20240320805). IMAGE DENOISING METHOD simplified abstract
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IMAGE DENOISING METHOD
Organization Name
Inventor(s)
IMAGE DENOISING METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320805 titled 'IMAGE DENOISING METHOD
The image denoising method described in the abstract involves several steps to remove noise from a noisy image using deep learning techniques.
- Extract a noise patch from the noisy image.
- Output a noise parameter by inputting the noise patch to a noise parameter estimation network (NPE-net).
- Generate imitated virtual noise based on the output noise parameter.
- Create a noisier image by adding the imitated virtual noise to the noisy image.
- Train a denoise deep learning model by inputting the noisy image and the noisier image as a pair.
- Input the noisy image to the trained denoise deep learning model.
- Output a denoised image obtained by removing noise from the noisy image using the trained denoise deep learning model.
Potential Applications: - Image processing applications - Photography software - Video editing tools
Problems Solved: - Removing noise from images - Enhancing image quality - Improving visual content
Benefits: - Enhanced image quality - Improved visual content - Efficient noise removal process
Commercial Applications: Title: Advanced Image Denoising Technology for Enhanced Visual Content This technology can be used in various commercial applications such as: - Photography software development - Video editing tools - Image processing software
Questions about Image Denoising Technology: 1. How does this method compare to traditional image denoising techniques? This method utilizes deep learning models to remove noise from images, offering potentially more accurate and efficient results compared to traditional techniques.
2. What are the key advantages of using virtual noise generation in the denoising process? Virtual noise generation allows for the creation of a noisier image for training the denoise deep learning model, leading to improved noise removal capabilities in the final denoised image.
Original Abstract Submitted
there is provided an image denoising method including extracting a noise patch from a noisy image, outputting a noise parameter by inputting the noise patch to a noise parameter estimation network (npe-net), generating imitated virtual noise based on the output noise parameter, generating a noisier image by adding the imitated virtual noise to the noisy image, training a denoise deep learning model by inputting the noisy image and the noisier image as a pair to the denoise deep learning model, inputting the noisy image to the trained denoise deep learning model, and outputting a denoise image obtained by removing noise from the noisy image by the trained denoise deep learning model.
