US 12,118,695 B2 · Noise estimation

Abstract

One example image processing device is provided. The example image processing device can include at least one processor and one or more memories coupled to the at least one processor and storing programming instructions for execution by the at least one processor to estimate noise in an image, the image being represented by a set of pixels and each pixel of the set of pixels having a value associated with it on each of one or more channels, where estimating the noise comprises processing, using a first trained model that detects stochastic noise, data derived from the image to form a first noise estimate, processing, using a second trained model that detects extreme pixel values, data derived from the image to form a second noise estimate, and combining the first and second noise estimates to form an aggregated noise estimate.

Claims15

Dependent claims

An image processing device, comprising:

at least one processor;
and
one or more memories coupled to the at least one processor and storing programming instructions for execution by the at least one processor to estimate noise in an image, the image being represented by a set of pixels, and each pixel of the set of pixels having a value associated with the pixel on each of one or more channels, wherein estimating the noise in the image comprises:
processing, using a first trained model that detects stochastic noise, data derived from the image to obtain a first noise estimate, wherein the first trained model detects at least one of Poisson noise or Gaussian noise;
processing, using a second trained model that detects extreme pixel values, data derived from the image to obtain a second noise estimate;
and
combining the first and second noise estimates to obtain an aggregated noise estimate.

4 more elements

An image processing device comprising at least one processor and one or more memories, the one or more memories storing in non-transient form instructions executable by the at least one processor to implement an image processing model to:

receive a plurality of pairs of images, wherein each pair of images represents a common scene and a first image of the pair which comprises more noise than a second image of the pair;
and
for the each pair of images,
process, using a first model that estimates stochastic noise in the images, data derived from the first image of the pair to obtain a first noise estimate;
process, using a second model that detects extreme pixel values, data derived from the first image of the pair to obtain a second noise estimate;
combine the first noise estimate and the second noise estimate to obtain an aggregated noise estimate;
estimate a difference between the second image of the pair and the first image of the pair de-noised in dependence on the aggregated noise estimate;
and
adapt the first and second models in dependence on the estimated difference.

6 more elements

Dependencies are inferred from the claim text.

Noise estimation

Life of this patent

Noise estimation

Life of this patent

Abstract

Claims15