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Image Filtering – Motion Blur

Image Filtering – Motion Blur. Ivan Dimitrov 508/2012. Filtering in g eneral. Widespread Suitable for Dataflow Easily upgradable. 2/11. Applying the filter. To apply the filter on the current pixel, you need to use his neighbor pixels. 2/10. 3/11. Sequential implementation.

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Image Filtering – Motion Blur

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  1. Image Filtering – Motion Blur Ivan Dimitrov 508/2012

  2. Filtering in general • Widespread • Suitable for Dataflow • Easily upgradable 2/11

  3. Applying the filter • To apply the filter on the current pixel, you need to use his neighbor pixels 2/10 3/11

  4. 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(intfilterX = 0; filterX < filterWidth; filterX++) //filtering for(intfilterY = 0; filterY < filterHeight; filterY++) { intimageX = (x - filterWidth / 2 + filterX + w) % w; intimageY = (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); } 4/11

  5. 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: DFEVarstream.offset(DFEVarsrc, int offset) ; 5/11

  6. Maxelerimplementation (2) • Due to the edge cases, we need to use the built-in counter function in Maxeler: DFEVarcontrol.count.simpleCounter(64); 6/11

  7. 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); 7/11

  8. 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()); } } 8/11

  9. Output 9/11

  10. Application • Image filtering is broadly used in web applications, social networks, chat applications… • AWS will soon rent servers with Maxeler cards • Social networks and other web applications can use Maxeler servers to process their images and save a lot of energy and time 10/11

  11. References [1] Phillips, D. , Image Processing in C, R & D Publications ,1994 [2]Milutinovic, V., editor, "Computer Architecture,“ (Chapter 9, DataFlow Computation, Dennis, J.,), North Holland, 1988. [3] Milutinovic, V., Salom, J., Trifunovic, N., Giorgi, R., "Guide to DataFlowSuperComputing," Springer, 2015. [4]Hurson, A., Milutinovic, V., editors, "DataFlowProcessing,“ Elsevier, 2015. 11/11

  12. Q&A

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