Patent ID: 11887279
Assignee: SHARIF UNIVERSITY OF TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. A system for denoising an image, the system comprising:
a memory having processor-readable instructions stored therein; and
one or more processors configured to access the memory and execute the processor-readable instructions, which, when executed by the one or more processors configures the one or more processors to perform a method, the method comprising:
training a fully convolutional neural network (FCN) by:
generating an nth training image of a plurality of training images, the nth training image comprising a plurality of training channels and a training array In of size Nh×Nw×C, where:
Nh is a height of the nth training image,
Nw is a width of the nth training image,
C is a number of the plurality of training channels,
1≤n≤N, and
N is a number of the plurality of training images;

initializing the FCN with a plurality of initial weights; and
repeating a first iterative process until a first termination condition is satisfied, comprising:
extracting an nth denoised training image from an output of the FCN by applying the FCN on the nth training image, the nth denoised training image comprising a denoised array În of size Nh×Nw×C;
generating a plurality of updated weights by minimizing a loss function comprising Σn=1N∥In−În| where |.| is an L1 norm, each updated weight of the plurality of updated weights associated with a respective initial weight of the plurality of initial weights; and
replacing the plurality of initial weights with the plurality of updated weights;

generating a reconstructed image by applying the FCN on the image.