IMAGE ENHANCEMENT AND RESTORATION

Pixel operations

Pixel point operations O(x,y) = M [ I(x,y) ]

Pixel operations Linear negative contrast enhancement (histogram sliding and stretching) Nonlinear photometric correction histogram equalization

Pseudocolor images

Group discussion Why to use pseudocolor mapping?

Color correction

Histogram equalization Before (1) After(2)

Histogram Equalization is a form of Image Enhancement particularly useful when images suffer from poor contrast. The histograms of such images would have relatively narrow curves around a certain range of pixel values and not at the others. For instance, the contrast of the image in Figure 1 is poor because there are no pixels in the bright areas. After Histogram Equalization, it can be observed from the histogram in Figure 2 that with a better utilization of the entire range of pixel values [0-255], the quality of the image is significantly enhanced. Histogram Equalisation:

Disadvantage of Applying Histogram Equalisation: As the name Histogram Equalization implies, this is a technique for obtaining a uniform histogram. The gray levels of the image subjected to histogram equalization always reach both extremities, 0 (black) and 255 (white). In other words, the process increases the dynamic range of the gray levels, consequently producing an increase in image contrast. This is not always suitable, as in this case, where increased visual graniness and "patchiness" are apparent in the output image.

Group discussion How to make the image histogram equalization?

Pixel point operations multiple image O(x,y)=I 1 (x,y) # I 2 (x,y) where # is arithmetic and logic operation as +, -, /, *, AND, OR, Exclusive-OR 1 Image Combining 2 Image Composition

1 Image Combining Subtraction images O(x,y) = I 1 (x,y) - I 2 (x,y)

Spectral Ratioing satellite image RGB+infrared live vegetation reflects a lot of infrared and very little red light energy Dead vegetation reflects a lot of red light energy and very little infrared energy

Image combining infrared component image (1) show live vegetation as bright object and dead vegetation as dark one red component image (2) shows just the opposite other objects mix perception, if just one spectrum is used Spectral rationing: O(x,y) = I 1 (x,y) / I 2 (x,y)

Image combining Temporal noise reduction O(x,y) = (I 1 (x,y) + I 2 (x,y)) / 2 O(x,y) = (I 1 (x,y) + I 2 (x,y) + …+ I n (x,y))/n Eliminates noise Equal to increasing the opening time of camera

Image Compositing Task: move part of the city image to the landscape image

Pixel group processing with convolution coefficient matrix 3*3, 5*5 … (mask,kernel) a b c d e f g h I O(x,y) = a*I(x-1,y-1)+b*I(x,y-1)+c*I(x+1,y-1) + d*I(x-1,y)+e*I(x,y)+f*I(x+1,y) + g*I(x-1,y+1)+h*I(x,y+1)+i*I(x+1,y+1)

More about convolution matrix:

Edge enhancement Shift & difference, Prewitt gradient, Laplace Sobel, Kirsch, Robinsson Named according to the inventors of the specific convolution, the mask varies.

Brightness slope

Prewitt Laplace directional edge enhancement NorthWest:

Line segment enhancement clean up the edges after the edge enhancement operation Example for horizontal line:

Sobel edge enhancement Horizontal and vertical mask 1 filter with horizontal mask 2 filter copy with vertical mask 3 add together

Vertical mask Horizontal maskResult Original

Median filter

Comparison between blurring and median filtering

Median filtering Softening 3*3 Salt&Pepper -noise

More on image noise

Group discussion What is the effect of enlargening the median filter window to 5*5, 7*7 etc. ? How do you think the Dust and scratches –operation is done in Photoshop?