Solution 1

          
 //filtra = new function
void filtra(float[] DATAF, float[] DATA, float a0, float a1) {

  for(int i = 3; i < DATA.length; i++){
    DATAF[i] = a0*DATA[i]+a1*DATA[i-1]+a0*DATA[i-2];//filtraggio FIR del secondo ordine simmetrico
  }
}
 
 	      
	    

Creando un ciclo for che reiteri l'operazione di filtraggio un certo numero di volte, si può verificare che l'effetto del filtraggio passa-basso viene accentuato. Questo risultato abbastanza intuitivo corrisponde al fatto che un filtraggio successivo attraverso m m filtri di ordine N N (nel nostro caso N=2 N 2 ) è equivalente ad un filtraggio attraverso un filtro di ordine mN m N

Solution 2

          
 
// smoothing Gaussian filter, adapted from REAS:
// http://www.processing.org/learning/examples/blur.html

size(200, 200); 
PImage a;  // Declare variable "a" of type PImage 
a = loadImage("vetro.jpg"); // Load the images into the program 
image(a, 0, 0); // Displays the image from point (0,0) 
 
int n2 = 5/2; 
int m2 = 5/2; 
int[][] output = new int[width][height]; 
float[][] kernel = { {1, 4, 7, 4, 1}, 
                     {4, 16, 26, 16, 4}, 
                     {7, 26, 41, 26, 7}, 
                     {4, 16, 26, 16, 4},
                     {1, 4, 7, 4, 1} }; 
 
for (int i=0; i<5; i++)
  for (int j=0; j< 5; j++)
    kernel[i][j] = kernel[i][j]/273; 
// Convolve the image 
for(int y=0; y<height; y++) { 
  for(int x=0; x<width/2; x++) { 
    float sum = 0; 
    for(int k=-n2; k<=n2; k++) { 
      for(int j=-m2; j<=m2; j++) { 
        // Reflect x-j to not exceed array boundary 
        int xp = x-j; 
        int yp = y-k; 
        if (xp < 0) { 
          xp = xp + width; 
        } else if (x-j >= width) { 
          xp = xp - width; 
        } 
        // Reflect y-k to not exceed array boundary 
        if (yp < 0) { 
          yp = yp + height; 
        } else if (yp >= height) { 
          yp = yp - height; 
        } 
        sum = sum + kernel[j+m2][k+n2] * red(get(xp, yp)); 
      } 
    } 
    output[x][y] = int(sum);  
  } 
} 
 
// Display the result of the convolution 
// by copying new data into the pixel buffer 
loadPixels(); 
for(int i=0; i<height; i++) { 
  for(int j=0; j<width/2; j++) { 
    pixels[i*width + j] = color(output[j][i], output[j][i], output[j][i]); 
  } 
} 
updatePixels(); 
 	      
	    

Solution 3

Ecco il filtro di mediana:

          
// smoothed_glass
// smoothing filter, adapted from REAS:
// http://www.processing.org/learning/examples/blur.html

size(210, 170); 
PImage a;  // Declare variable "a" of type PImage 
a = loadImage("vetro.jpg"); // Load the images into the program 
image(a, 0, 0); // Displays the image from point (0,0) 
 
// corrupt the central strip of the image with random noise 
float noiseAmp = 0.1;
loadPixels(); 
for(int i=0; i<height; i++) { 
  for(int j=width/4; j<width*3/4; j++) { 
    int rdm = constrain((int)(noiseAmp*random(-255, 255) + 
      red(pixels[i*width + j])), 0, 255); 
    pixels[i*width + j] = color(rdm, rdm, rdm);
  } 
} 
updatePixels(); 
  
int[][] output = new int[width][height]; 
int[] sortedValues = {0, 0, 0, 0, 0};
int grayVal;
  
// Convolve the image 
for(int y=0; y<height; y++) { 
  for(int x=0; x<width/2; x++) { 
    int indSort = 0;
    for(int k=-1; k<=1; k++) { 
      for(int j=-1; j<=1; j++) { 
        // Reflect x-j to not exceed array boundary 
        int xp = x-j; 
        int yp = y-k; 
        if (xp < 0) { 
          xp = xp + width; 
        } else if (x-j >= width) { 
          xp = xp - width;  
        } 
        // Reflect y-k to not exceed array boundary 
        if (yp < 0) { 
          yp = yp + height; 
        } else if (yp >= height) { 
          yp = yp - height; 
        } 
        if ((((k != j) && (k != (-j))) ) || (k == 0)) { //cross selection
          grayVal = (int)red(get(xp, yp)); 
          indSort = 0;
          while (grayVal < sortedValues[indSort]) {indSort++; }          
          for (int i=4; i>indSort; i--) sortedValues[i] = sortedValues[i-1];
          sortedValues[indSort] = grayVal;
        } 
      } 
    } 
    output[x][y] = int(sortedValues[2]);  
    for (int i=0; i< 5; i++) sortedValues[i] = 0;
  } 
} 
 
// Display the result of the convolution 
// by copying new data into the pixel buffer 
loadPixels(); 
for(int i=0; i<height; i++) { 
  for(int j=0; j<width/2; j++) { 
    pixels[i*width + j] = 
      color(output[j][i], output[j][i], output[j][i]);
  } 
} 
updatePixels();