Implement Anisotropic Diffusion on CUDA platform 1 thread handle 1 pixel Dividing the image to multiple sub-regions, process them parallely to exploit multiple cores

1. USING PARALLEL PROGRAMMING TO
IMPROVE PERFORMANCE OF IMAGE
PROCESSING
Chan Le – KAIST ’13

2. INTRODUCTION
 Me:
 Chan Le – 3rd year undergraduate student
 Double major in Computer Science & Management Science
 A Vietnamese - KAIST ’13
 Professor:
 Won-Ki Jeong
 GPU-accelerated large-scale biomedical image processing
 Project:
 Apply parallel programming to improve performance of image
processing

3. MOTIVATION
 Biomedical researches work with images
 Really big images
 Take long time to process
 Raw images are hard to analyze & use for research
 Really noisy sometimes
 Need to preprocess before using
 Image preprocessing using serial algorithms are
slow
 Nowadays, parallel computing are developing
 Thanks to the popularity of multi-core CPUs and GPUs

4. RELATED WORKS: USING PDE IN NOISE-
REDUCTION
IN
(x,y+1
ΔW = IW – It
)
ΔN
IW ΔW It ΔE IE
(x-1,y) (x,y) (x+1,y)
ΔS
IS
(x,y-
1)
 Heat equation
 At pixel every (x,y) of the image at the time t:
I =It+ΔI
 t+1
ΔI = (ΔW+ ΔN+ ΔE+ ΔS) / 4

5. RELATED WORKS: ANISOTROPIC DIFFUSION
 Paper: Scale-space and edge detection using
anisotropic diffusion (Pietro Perona & Jitendra Malik, 1990)
 Basic idea: Adding coefficient to each ΔW,ΔN,ΔS,ΔE
 .
 
/4
 How to calculate each c?
C=
C=

6. RELATED WORKS

7. NVIDIA CUDA
 Serial vs Parallel program
 Thread: unit of processing
 In the past: CPU has only 1 core -> 1 thread at a time
 Nowadays: multi-cores -> multiple thread at a time
 CUDA™ is a parallel computing platform and
programming model invented by NVIDIA.
 http://www.nvidia.com/object/cuda_home_new.html
 How could it helps?
 CPU: 1-6 cores
 GPU: hundreds
  improve performance by the scale of 10 to 100, depends on
the algorithm

8. MY IMPLEMENTATION
 Implement Anisotropic
Diffusion on CUDA
platform
 1 thread handle 1 pixel
 Dividing the image to
multiple sub-regions,
process them parallely to
exploit multiple cores

9. SOME RESULT – SMALL

10. SOME RESULT – SMALL

11. SOME RESULT – MEDIUM

12. SOME RESULT – MEDIUM

13. BENCHMARK
 100 times iteration

14. CONCLUSION
 The result of this project could be use to help
improving quality of images before using.
 Utilizing GPU computing power could improve the
performance of your program by 100-200 times
 Partial Differential Equations are good choices
when design parallel algorithm
 However, the performance is limited by the GPU’s
memory size