Image Filtering – Motion Blur - PowerPoint Presentation

Slide1

Image Filtering – Motion Blur

Ivan

Dimitrov

508/2012Slide2

Filtering in g

eneral

Widespread

Suitable for Dataflow Easily upgradable

2/11Slide3

Applying the filter

To apply the filter on the current pixel, you need to use his neighbor pixels

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3/11Slide4

Sequential implementation

for(

int

x = 0; x < w; x++) for(int y = 0; y < h; y++) {

double red = 0.0, green = 0.0, blue = 0.0; //multiply every value of the filter with corresponding image pixel

for(int filterX = 0;

filterX

<

filterWidth

;

filterX

++)

//filtering

for(int filterY = 0; filterY < filterHeight; filterY++) { int imageX = (x - filterWidth / 2 + filterX + w) % w; int imageY = (y - filterHeight / 2 + filterY + h) % h; red += image[imageX][imageY].r * filter[filterX][filterY]; // red channel green += image[imageX][imageY].g * filter[filterX][filterY]; // green channel blue += image[imageX][imageY].b * filter[filterX][filterY]; //blue channel } result[x][y].r = min(max(int(factor * red + bias), 0), 255); //result image result[x][y].g = min(max(int(factor * green + bias), 0), 255); result[x][y].b = min(max(int(factor * blue + bias), 0), 255); }

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Maxeler implementation (1)

We need to use more than one element at a time

because of how filtering works.

So we need to use Maxeler’s built-in function for getting previous elements of the array:

DFEVar stream.offset(

DFEVar

src

,

int

offset)

;

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Maxeler implementation

(2)

Due to the edge cases, we need to use the built-in counter function in

Maxeler:

DFEVar control.count.simpleCounter(64);

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Maxeler implementation

(3)

class

BlurKernel extends Kernel { protected

BlurKernel(KernelParameters parameters) { super(parameters);

DFEVar inputImg1 = io.input("array1", dfeInt(32));

DFEVar

count =

control.count.simpleCounter

(64

);

DFEVar First1 = stream.offset(inputImg1, -513); ); DFEVar Last1 = stream.offset(inputImg1, 513); DFEVar inputImg2 = io.input("array2", dfeInt(32)); DFEVar First2 = stream.offset(inputImg2, -513);//picsize 512x512 DFEVar Last2 = stream.offset(inputImg2, 513); DFEVar inputImg3 = io.input("array3", dfeInt(32)); DFEVar First3 = stream.offset(inputImg3, -513); DFEVar Last3 = stream.offset(inputImg3, 513); 7/11Slide8

Maxeler implementation (4)

inputImg1 = (count>512) ? count<(512*512-512) ? ((First1 + Last1 + inputImg1) /3):0 : 0;

inputImg2

= (count>512) ? count<(512*512-512) ? (First2 + Last2 + inputImg2) /3 :0 :0;

inputImg3 = (count>512) ? count<(512*512-512) ? (First3 + Last3 + inputImg3) /3 :0:0; io.output

("outImage1", inputImg1, inputImg1.getType()); io.output("outImage2", inputImg2, inputImg2.getType()); io.output

("outImage3", inputImg3, inputImg3.getType());

}

}

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Output

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Application

10/11

Image filtering is broadly used in web applications, social networks, chat applications…

AWS will soon rent servers with

M

axeler

cards

Social networks and other web applications can use

M

axeler

servers to process their images and save a lot of energy and timeSlide11

References

[1]

Image Processing in

C, Dwayne Phillips, 1994[2] Guide to DataFlow

SuperComputing, V. Milutinovic, J. Salom, N.

Trifunovic, R. Giorgi, 2015[3]

Blur Motion,

https

://

en.wikipedia.org/wiki/Motion_blur

, 1.1.2016

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Q&A