Learning based discrete cosine noise filter

Patent metadata

Field	Value
Publication number	US-2023252604-A1
Application number	US-202217665294-A
Country	US
Kind code	A1
Filing date	Feb 4, 2022
Priority date	Feb 4, 2022
Publication date	Aug 10, 2023
Grant date	—

Abstract

A method includes filtering an input image through a discrete cosine transform based noise filter (DCT-NF). The DCT-NF converts the input image from an original space into a perceptual space, applying gamma correction. The DCT-NF separates luminance channels of the input image from chroma channels of the input image. The DCT-NF divides the input image into overlapping patches and computes DCT transform of the patches. Each patch is a different partial portion of the input image. The DCT-NF suppresses patches that include an input DCT coefficient within a threshold range. The DCT-NF applies an inverse discrete cosine transform (IDCT) to the suppressed patches and remaining overlapping patches that include an input DCT coefficient outside the threshold range. The DCT-NF re-combines luminance and chroma channels of the IDCT-transformed patches. The DCT-NF generates a DCT noise-filtered output image by re-converting the IDCT-transformed patches to the original space by applying inverse gamma correction.

First claim

What is claimed is:

1 . A method comprising:
receiving an input image from an image capturing device; filtering the received input image by applying a discrete cosine transform based noise filtering (DCT-NF) process, the DCT-NF process comprising:
converting the input image from an original space into a perceptual space by applying a gamma correction;
separating luminance channels of the input image from chroma channels of the input image;
dividing the input image into overlapping patches, each patch being a different partial portion of the input image;
computing a discrete cosine transform (DCT) of the overlapping patches;
among the overlapping patches, suppressing patches that include an input DCT coefficient within a threshold range;
applying an inverse discrete cosine transform (IDCT) to the suppressed patches and to remaining overlapping patches that include an input DCT coefficient outside of the threshold range;
re-combining luminance channels and chroma channels of the IDCT-transformed patches to form a color image; and
generating a DCT noise-filtered output image by re-converting the color image to the original space by applying an inverse gamma correction; and
outputting the DCT noise-filtered output image.

2 . The method of claim 1 , wherein the DCT-NF process further comprises:
tuning threshold parameters and suppression parameters defining the threshold range based on a power spectral density obtained from a live image capture.

3 . The method of claim 1 , wherein at least one of threshold parameters or suppression parameters are learned from training data using a machine learning technique.

4 . The method of claim 1 , wherein:
separating luminance channels comprises converting the input image from an RGB domain to a YUV domain; and re-combining the luminance channels and the chroma channels comprises converting from the YUV domain to the RGB domain.

5 . The method of claim 4 , wherein the DCT-NF process further comprises:
separately filtering a U channel and a V channel of the converted input image through a low computational filter.

6 . The method of claim 4 , wherein the DCT-NF process further comprises:
classifying patches among the overlapping patches; and suppressing the classified patches based on threshold parameters and suppression parameters that vary spatially based upon the classification.

7 . The method of claim 1 , wherein the overlapping patches include a first grid of M×M patches that at least partially overlaps a second grid of M×M patches.

8 . The method of claim 1 , wherein the threshold range is defined by a first slope for input DCT coefficient values within a first suppression range, and a second slope for input DCT coefficient values within a second suppression range.

9 . The method of claim 1 , further comprising computing an average of patches for reconstruction within the color image.

10 . An electronic device comprising:
a display; and a processor configured to:
receive an input image from an image capturing device;
filter the received input image by applying a discrete cosine transform based noise filtering (DCT-NF) process, wherein the processor is configured to perform the DCT-NF process by:
converting the input image from an original space into a perceptual space by applying a gamma correction;
separating luminance channels of the input image from chroma channels of the input image;
dividing the input image into overlapping patches, each patch being a different partial portion of the input image;
computing a discrete cosine transform (DCT) of the overlapping patches;
among the overlapping patches, suppressing patches that include an input DCT coefficient within a threshold range;
applying an inverse discrete cosine transform (IDCT) to the patches and to remaining overlapping patches that include an input DCT coefficient outside of the threshold range;
re-combining luminance channels and chroma channels of the IDCT-transformed patches to form a color image; and
generating a DCT noise-filtered output image by re-converting the color image to the original space by applying an inverse gamma correction; and

output the DCT noise-filtered output image for displaying on the display.

11 . The electronic device of claim 10 , wherein the DCT-NF process further comprises:
tuning threshold parameters and suppression parameters defining the threshold range based on a power spectral density obtained from a live image capture.

12 . The electronic device of claim 10 , wherein at least one of threshold parameters or suppression parameters are learned from training data using a machine learning technique.

13 . The electronic device of claim 10 , wherein the processor is configured to:
separate luminance channels by converting the input image from an RGB domain to a YUV domain; and re-combine the luminance channels and the chroma channels by converting from the YUV domain to the RGB domain.

14 . The electronic device of claim 13 , wherein the DCT-NF process further comprises:
separately filtering a U channel and a V channel of the converted input image through a low computational filter.

15 . The electronic device of claim 13 , wherein the DCT-NF process further comprises:
classifying patches among the overlapping patches; and suppressing the classified patches based on threshold parameters and suppression parameters that vary spatially based upon the classification.

16 . The electronic device of claim 10 , wherein the overlapping patches include a first grid of M×M patches that at least partially overlaps a second grid of M×M patches.

17 . The electronic device of claim 10 , wherein the threshold range is defined by a first slope for input DCT coefficient values within a first suppression range, and a second slope for input DCT coefficient values within a second suppression range.

18 . The electronic device of claim 10 , wherein the processor is configured to compute an average of patches for reconstruction within the color image.

19 . A non-transitory computer readable medium embodying a computer program, the computer program comprising computer readable program code that when executed causes at least one processing device to:
receive an input image from an image capturing device; filter the received input image by applying a discrete cosine transform based noise filtering (DCT-NF) process, the DCT-NF process comprising:
converting the input image from an original space into a perceptual space by applying a gamma correction;
separating luminance channels of the input image from chroma channels of the input image;
dividing the input image into overlapping patches, each patch being a different partial portion of the input image;
computing a discrete cosine transform (DCT) of the overlapping patches;
among the overlapping patches, suppressing patches that include an input DCT coefficient within a threshold range;
applying an inverse discrete cosine transform (IDCT) to the suppressed patches and to remaining overlapping patches that include an input DCT coefficient outside of the threshold range;
re-combining luminance channels and chroma channels of the IDCT-transformed patches to form a color image; and
generating a DCT noise-filtered output image by re-converting the color image to the original space by applying an inverse gamma correction; and
output the DCT noise-filtered output image.

20 . The non-transitory computer readable medium of claim 19 , wherein the DCT-NF proces

Assignees

• Samsung Electronics Co Ltd

Inventors

• Nadir Zeeshan
• Glotzbach John W
• Sheikh Hamid R

Classifications

• G06T5/10

  using non-spatial domain filtering · CPC title

• G06T2207/20056

  Discrete and fast Fourier transform, [DFT, FFT] · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T5/70 Primary

  Denoising; Smoothing · CPC title

• G06T5/60

  using machine learning, e.g. neural networks · CPC title