Fast Computation of a Laplacian Pyramid in a Parallel Computing Environment

Patent metadata

Field	Value
Publication number	US-2016148359-A1
Application number	US-201414548520-A
Country	US
Kind code	A1
Filing date	Nov 20, 2014
Priority date	Nov 20, 2014
Publication date	May 26, 2016
Grant date	—

Abstract

A computer-implemented method for calculating a Laplacian pyramid in an image processing system comprising a parallel computing platform includes constructing a first layer of a Gaussian pyramid based on an original image. A plurality of Laplacian pyramid layers are constructed using a plurality of device kernels executing on a graphical processing device included in the parallel computing platform. Each respective Laplacian pyramid layer is constructed by a process which includes using one or more first device kernels to calculate a Gaussian pyramid layer based on a immediately preceding Gaussian pyramid layer and using one or more second device kernels to calculate the respective Laplacian pyramid layer based on the immediately preceding Gaussian pyramid layer in parallel with calculation of the Gaussian pyramid layer.

First claim

What is claimed is:

1 . A computer-implemented method for calculating a Laplacian pyramid in an image processing system comprising a parallel computing platform, the method comprising:
constructing a first layer of a Gaussian pyramid based on an original image; constructing a plurality of Laplacian pyramid layers using a plurality of device kernels executing on a graphical processing device included in the parallel computing platform, wherein each respective Laplacian pyramid layer is constructed by a process comprising:
using one or more first device kernels to calculate a Gaussian pyramid layer based on a immediately preceding Gaussian pyramid layer; and
using one or more second device kernels to calculate the respective Laplacian pyramid layer based on the immediately preceding Gaussian pyramid layer in parallel with calculation of the Gaussian pyramid layer.

2 . The method of claim 1 , wherein each respective Gaussian pyramid layer is calculated using a single operation on a respective computation unit, the single operation combining:
upsampling the immediately preceding Gaussian pyramid layer to yield a upsampled layer, and convolving the upsampled layer with a Gaussian filter to yield the Gaussian pyramid layer.

3 . The method of claim 2 , wherein the single operation further comprises downsampling the Gaussian pyramid layer.

4 . The method of claim 2 , wherein convolving the upsampled layer with the Gaussian filter to yield the Gaussian pyramid layer comprises:
computing a plurality of horizontal convolutions using a horizontal filter and the upsampled layer; and computing a plurality of vertical convolutions using a vertical filter and the upsampled layer.

5 . The method of claim 4 , wherein the plurality of horizontal convolutions and the plurality of vertical convolutions are computed in separate device kernels included in the one or more first device kernels.

6 . The method of claim 1 , wherein each respective Laplacian pyramid layer is calculated using a single operation on a respective computation unit, the single operation comprising:
upsampling the immediately preceding Gaussian pyramid layer to yield an upsampled layer; smoothing the upsampled layer to yield a smoothed upsampled layer; and subtracting the smoothed upsampled layer from the original image or from a corresponding layer of the Gaussian pyramid to yield the respective Laplacian pyramid layer.

7 . The method of claim 6 , wherein smoothing the upsampled layer to yield the smoothed upsampled layer comprises:
computing a plurality of horizontal convolutions using a horizontal filter and the upsampled layer; and computing a plurality of vertical convolutions using a vertical filter and the upsampled layer.

8 . The method of claim 4 , wherein the plurality of horizontal convolutions and the plurality of vertical convolutions are computed in separate device kernels included in the one or more second device kernels.

9 . The method of claim 1 , wherein two or more of the plurality of Laplacian pyramid layers are calculated in parallel using the parallel computing platform.

10 . A computer-implemented method for calculating a Laplacian pyramid in an image processing system comprising a host computing unit and a graphical processing device, the method comprising:
copying an original image from a host memory at the host computing unit to a portion of device memory on the graphical processing device; constructing a first layer of a Gaussian pyramid based on the original image; executing a plurality of device kernels on the graphical processing device to calculate the Laplacian pyramid, wherein each respective layer in the Laplacian pyramid is calculated using a set of device kernels comprising:
one or more first kernels configured to calculate a Gaussian pyramid layer based on a immediately preceding Gaussian pyramid layer, and
one or more second kernels configured to calculate a respective Laplacian pyramid layer based on the immediately preceding Gaussian pyramid layer; and
copying the Laplacian pyramid from the portion of device memory on the graphical processing device to the host memory.

11 . The method of claim 10 , further comprising:
prior copying the original image to the portion of device memory, allocating the portion of device memory on the graphical processing device based on a size of the original image; and after executing the plurality of device kernels, deallocating the portion of device memory.

12 . The method of claim 10 , wherein the set of device kernels executes in parallel on the graphical processing device.

13 . The method of claim 10 , wherein the first layer of the Gaussian pyramid is constructed at the graphical processing device using a third kernel configured to calculate the first layer of the Gaussian pyramid based on the original image.

14 . The method of claim 10 , wherein each respective first kernel is configured to calculate the Gaussian pyramid layer based on the immediately preceding Gaussian pyramid layer using a single operation combining:
upsampling the immediately preceding Gaussian pyramid layer to yield an upsampled layer; convolving the upsampled layer with a Gaussian filter to yield the Gaussian pyramid layer; and downsampling the Gaussian pyramid layer.

15 . The method of claim 10 , wherein each respective second kernel is configured to calculate the respective Laplacian pyramid layer using a single operation combining:
upsampling the immediately preceding Gaussian pyramid layer to yield an upsampled layer; smoothing the upsampled layer to yield a smoothed upsampled layer; and subtracting the smoothed upsampled layer from the original image to yield the respective Laplacian pyramid layer.

16 . The method of claim 10 , wherein each respective device kernel in the plurality of device kernels is executed independently by a distinct grid of thread blocks on the graphical processing device.

17 . The method of claim 10 , wherein two or more of the plurality of Laplacian pyramid layers are calculated in parallel using the parallel computing platform.

18 . A system for calculating a Laplacian pyramid, the system comprising:
a processor configured to construct a first layer of a Gaussian pyramid based on an original image; and a graphical processing device configured to execute a plurality of device kernels to calculate the Laplacian pyramid, wherein each respective Laplacian pyramid layer is calculated using a set of device kernels comprising:
one or more first device kernels configured to calculate a Gaussian pyramid layer based on a immediately preceding Gaussian pyramid layer, and
one or more second device kernels configured to calculate the respective Laplacian pyramid layer based on the immediately preceding Gaussian pyramid layer.

19 . The system of claim 18 , wherein each respective first device kernel is configured to calculate the Gaussian pyramid layer based on the immediately preceding Gaussian pyramid layer using a single operation combining:
upsampling the immediately preceding Gaussian pyramid layer to yield an upsampled layer; convolving the upsampled layer with a Gaussian filter to yield the Gaussian pyramid layer; and downsampling the Gaussian pyramid layer.

20 . The system of claim 18 , wherein each respective second device kernel is configured to the respective Laplacian pyramid layer using a single operation combining steps of:
upsampling the immediately preceding Gaussian pyramid layer to yield an ups

Assignees

• Siemens Medical Solutions

Inventors

• Gnedin Marianna

Classifications

• G06T2207/20182

  Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering · CPC title

• G06T5/20 Primary

  using local operators · CPC title

• G06T5/002

  Physics · mapped topic

• G06T2207/20016

  Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform · CPC title