Digital Image Processing: Image Enhancement - Duong Anh Duc

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Digital Image Processing: Image Enhancement - Duong Anh Duc presents about Image Enhancement; Point Operations; Image Negative; Contrast Stretching; Compression of Dynamic Range; Image Averaging for noise reduction; Some Averaging Filters; Some Typical Histograms.

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Nội dung Text: Digital Image Processing: Image Enhancement - Duong Anh Duc

Digital Image Processing Image Enhancement 21/11/15 Duong Anh Duc - Digital Image Processing 1
Image Enhancement  To process an image so that output is “visually better” than the input, for a specific application.  Enhancement is therefore, very much dependent on the particular problem/image at hand.  Enhancement can be done in either: – Spatial domain: operate on the original image g(m,n) = T[f(m,n)] – Frequency domain: operate on the DFT of the original image G(u,v) = T[F(u,v)], where F(u,v) = F[f(m,n)], and G(u,v) = F [g(m,n)], 21/11/15 Duong Anh Duc - Digital Image Processing 2
Image Enhancement Techniques Point Operations Mask Operations Transform Operations Coloring Operations • Image Negative • Smoothing • Low pass • False Coloring • Contrast operations Filtering • Full color Stretching • Median Filtering • Hi pass Filtering Processing • Compression of • Sharpening • Band pass dynamic range operations Filtering • Graylevel slicing • Derivative • Homomorphic • Image operations Filtering Subtraction • Histogram • Histogram • operations operations Image Averaging • Histogram operations 21/11/15 Duong Anh Duc - Digital Image Processing 3
Point Operations  Output pixel value g(m, n) at pixel (m, n) depends only on the input pixel value at f(m, n) at (m, n) (and not on the neighboring pixel values).  We normally write s = T(r), where s is the output pixel value and r is the input pixel value.  T is any increasing function that maps [0,1] into [0,1]. 21/11/15 Duong Anh Duc - Digital Image Processing 4
Image Negative T(r) = s = L-1-r, L: max grayvalue 21/11/15 Duong Anh Duc - Digital Image Processing 5
Negative Image 21/11/15 Duong Anh Duc - Digital Image Processing 6
Contrast Stretching  Increase the dynamic range of grayvalues in the input image.  Suppose you are interested in stretching the input intensity values in the interval [r1, r2]:  Note that (r1- r2) < (s1- s2). The grayvalues in the range [r1, r2] is stretched into the range [s1, s2]. 21/11/15 Duong Anh Duc - Digital Image Processing 7
Contrast Stretching  Special cases: – Thresholding or binarization r1 = r2 , s1 = 0 and s2 = 1 – Useful when we are only interested in the shape of the objects and on on their actual grayvalues. 21/11/15 Duong Anh Duc - Digital Image Processing 8
Contrast Stretching 21/11/15 Duong Anh Duc - Digital Image Processing 9
Contrast Stretching  Special cases (cont.): – Gamma correction: S1 = 0, S2 = 1 and 0, r r1 g r r1 T r , r1 r r2 r2 r1 1, r r2 21/11/15 Duong Anh Duc - Digital Image Processing 10
Contrast Stretching Gamma correction 21/11/15 Duong Anh Duc - Digital Image Processing 11
Compression of Dynamic Range  When the dynamic range of the input grayvalues is large compared to that of the display, we need to “compress” the grayvalue range --- example: Fourier transform magnitude.  Typically we use a log scale. s = T(r) = c log(1+ r ) 21/11/15 Duong Anh Duc - Digital Image Processing 12
Compression of Dynamic Range Saturn Image Mag. Spectrum Mag. Spectrum in log scale 21/11/15 Duong Anh Duc - Digital Image Processing 13
Compression of Dynamic Range  Graylevel Slicing: Highlight a specific range of grayvalues. 21/11/15 Duong Anh Duc - Digital Image Processing 14
Compression of Dynamic Range  Example: Highlighted Image (no background) Original Image Highlighted Image (with background) 21/11/15 Duong Anh Duc - Digital Image Processing 15
Compression of Dynamic Range  Bitplane Slicing: Display the different bits as individual binary images. 21/11/15 Duong Anh Duc - Digital Image Processing 16
Compression of Dynamic Range 21/11/15 Duong Anh Duc - Digital Image Processing 17
Image Subtraction  In this case, the difference between two “similar” images is computed to highlight or enhance the differences between them: g(m,n) = f1(m,n)-f2(m,n)  It has applications in image segmentation and enhancement 21/11/15 Duong Anh Duc - Digital Image Processing 18
Example: Mask mode radiography f1(m, n): Image before dye injection g(m, n): Image after dye injection, f2(m, n): Image after dye injection followed by subtraction 21/11/15 Duong Anh Duc - Digital Image Processing 19
Image Averaging for noise reduction  Noise is any random (unpredictable) phenomenon that contaminates an image.  Noise is inherent in most practical systems: – Image acquisition – Image transmission – Image recording  Noise is typically modeled as an additive process: g(m,n) = f(m,n) + (m,n) Noisy Noise-free Noise Image Image 21/11/15 Duong Anh Duc - Digital Image Processing 20