TECHNIQUES FOR ROBUST IMAGE DENOISING

Number: 20210150674
Published: 2021-05-20
Filed: 2020-02-19
Assignee: DISNEY ENTERPRISES, INC.
Inventors: CAI; Zhilin, AYDIN; Tunc Ozan, MANZI; Marco, OZTIRELI; Ahmet Cengiz
CPC: G06T5/70; G06T5/60; G06T5/50
Verdict: Set aside generic image denoising, no creative hook
Source: Google Patents · FreePatentsOnline

Abstract

Techniques are disclosed for training and applying a denoising model. The denoising model includes multiple specialized denoisers and a generalizer, each of which is a machine learning model. The specialized denoisers are trained to denoise images associated with specific ranges of noise parameters. The generalizer is trained to generate per-pixel denoising kernels for denoising images associated with arbitrary noise parameters using outputs of the specialized denoisers. Subsequent to training, a noisy image, such as a live-action image or a rendered image, can be denoised by inputting the noisy image into the specialized denoisers to obtain intermediate denoised images that are then input, along with the noisy image, into the generalizer to obtain per-pixel denoising kernels, which can be normalized and applied to denoise the noisy image.

Background

BACKGROUND Technical Field

Embodiments of the present disclosure relate generally to image processing and denoising and, more specifically, to techniques for robust image denoising. Description of the Related Art

Image noise refers to random variation in the color and/or brightness within images. Noise is a typical byproduct in images that are rendered using Monte Carlo rendering techniques. Images (i.e., photographs) captured by cameras can also contain noise due to the mechanics of camera sensors. For example, the quality of images captured in low-lighting conditions can be adversely affected by noise.

Image denoising is the process of removing noise from an image. Conventional approaches for image denoising utilize either specialized denoisers or “blind” denoisers. Specialized denoisers, such as deep denoising networks, can be trained to remove specific types of noise from images, where the noise is typically within narrow ranges of one or more noise parameters, such as additive white Gaussian noise having a certain magnitude. However, the performance of specialized denoisers drops rapidly for images having noise with noise parameters that are different than the training noise parameters.

In contrast to specialized denoisers, blind denoisers are typically trained using images having noise with diverse noise parameters. Although blind denoisers can be robust to different noise parameters, such robustness comes at the cost of overall denoising qua

Claims

1. A computer-implemented method for denoising an image, the method comprising: processing the image using a plurality of first denoisers to generate a plurality of first denoised images; wherein each first denoiser is trained to denoise images associated with at least one noise parameter; and processing the image and the plurality of first denoised images using a second denoiser to generate a second denoised image. || 10. A computer-implemented method for training denoisers, the method comprising: training each first denoiser included in a plurality of first denoisers using a respective set of images associated with at least one noise parameter and ground truth images corresponding to images included in the respective set of images; and training a second denoiser using an additional set of images, ground truth images corresponding to images included in the additional set of images, and a plurality of denoised images, wherein the plurality of denoised images is generated by processing the additional set of images using the plurality of first denoisers. || 18. A computer-readable storage medium including instructions that, when executed by a processing unit, cause the processing unit to perform steps for denoising an image, the steps comprising: processing the image using a plurality of first denoisers to generate a plurality of first denoised images, wherein each first denoiser is trained to denoise images associated with at least one noise parameter; and processing the image and the plurality of first denoised images using a second denoiser to generate a second denoised image.