Samsung electronics co., ltd. (20240320805). IMAGE DENOISING METHOD simplified abstract

IMAGE DENOISING METHOD

Organization Name

samsung electronics co., ltd.

Inventor(s)

Nohong Kwak of Suwon-si (KR)

Donyun Kim of Suwon-si (KR)

Jiwon Kang of Suwon-si (KR)

Kihyun Kim of Suwon-si (KR)

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.