Last updated 2015. 03. 22 Heejune Ahn, SeoulTech Pixels Last updated 2015. 03. 22 Heejune Ahn, SeoulTech

Outline What is Pixel-level processing? Pixel level Operations Transform Histogram HE (Histogram Equalization) HA (histogram Matching) Trhesholding

1. Pixel Processing in pixel level Information of pixel level Not using information of neighbor pixels Information of pixel level visual color/intensity : camera data IR (infrared) : emission from heat object, night-vision, surveillance Medical image : density of tissue, CT (computed tomography), MRI (magnetic resonance imaging), 3D (stack of 2Ds) Ladar/sonar 3-D imaging: 3D scanning, depth map Scientific image

2. Operations upon pixels Individual, pixel-by-pixel Iout (n,m) = f (Iin(n,m)) E.g. Iin(n,m) + IB(n,m) or Iin(n,m) + C Arithmetic operation contrast adjustment : imadd(I, const)

Blending(mixing): imadd(I1, I2) Substraction: imsubstract(I1, I2) Difference : imabsdiff(I1, I2)

Multiplication, division : immultiply(I, const), imdivide(I, const) Saturation issue Overflow & underflow problem in range [0, 255] matlab “im” functions handle the saturation

Logical operations Thresholding Mostly for binary image NOT: imcomplement(I) OR, XOR, AND, NAND, NOR, NXOR Thresholding Gray scale to binary image : im2bw(I, thres) Io = 1 if Iin > T or 0 o.w. Used for extract fg from bg variations

Thresholding for simple and complex image

4. transform Mostly for improving the contrast of images (dynamic range) Logarithmic transform Increase the contrast in “low” values

Exponential transform Increase contrast in high values

Gamma (power) flexible r < 1 : log-style r > 1 : exp-style

4. Histograms Histogram h(x) = # of pixels whose value is x. pmf (x) = h(x) / # of pixels

Thresholding Global Threshold selection Bi-modal distribution (F3.12, previous slide) : easy to select Multi-modal/complicated (F3.13) : not easy

Otsu’s Method: global optimal algorithm Threshold that minimizes the intra class standard variance (a clustering algorithm) level =graythresth(Img) in MATLAB Minimizing Intra-class variances Maximizing Inter-class variances

Adaptive thresholding Reason: Illumination is not uniform, multiple objects T(n, m) = f ( W[n,m] ) Threashold value “adapts” neighbors of pixel (n,m), W. E.g of function mean, median, floor((max – min/2) +C still noise f median + margin T orignal

(value – c) (a-b)/(c-d) + a Contrast Control To enhance visual perception Linear contrast stretching Tips: outlier problem Use c at 5%, d at 95% [c, d] [b= 0, a = 255] (value – c) (a-b)/(c-d) + a

Histogram equalization Resultant histogram is flat/equal Nonlinear & dependent image histogram Global method proof: Iin ~ px(x) Iout ~ py(y) y = f(x)

Example Not exactly flat (in discrete values)

Histogram Matching Generalization of histogram equalization map output image’s histogram to a specific function. Iin ~ px(x) ~ Cx(x) Iout ~ py(y) ~ Cy(y) y = f(x) f(x) = C-1z[Cx(x)]

Adaptive histogram equalizer Similar reason as adaptive thresholding Local histogram generation Sliding windows method Tile-based method Low computation Blocking effects outter window inner window

Pizer’s approach Weighted histogram I = histogram for i

Contrast limiting MATLAB Not to over-amplificate noise pixels Concept J = adapthisteq (I, [param1,val1]...) Params ClipLimit : [0:1] (0.01) NumTiles: (8) Distribution Target distist. (uniform) Rayleigh exponential uniform

A Real Application Budapest Castle (Hungary) Taken by Galaxy S5 in the evening rgb AHE gray/hsv GHE