Lecture No Image Enhancement in SpaPal Domain (course: Computer Vision)

Transcription

1 Lecture No Image Enhancement in SpaPal Domain (course: Computer Vision) e- mail: Department of So9ware Engineering, Mehran UET Jamshoro, Sind, Pakistan

2 Principle objecpves To Process an image so that the result will be more suitable than the original image for a specific applicapon The suitableness is up to each applicapon A method which is quite useful for enhancing an image may not necessarily be the best approach for enhancing another images

3 SpaPal Domain: (image plane) 2 Domains Techniques are based on direct manipulapon of pixels in an image For example: Point processing, Histogram technique (that is also point processing) and Mask processing Frequency Domain: Techniques are based on modifying the Fourier transform of an image There are some enhancement techniques based on various combinapons of methods from these two categories

4 For Human VisualizaPon Good Images The visual evaluapon of image quality is a highly subjecpve process It is hard to standardize the definipon of a good image For Machine PercepPon The evaluapon task is easier A good image is one which gives the best machine recognipon results A certain amount of trial and error usually is required before a parpcular image enhancement approach is selected

5 SpaPal Domain It is the normal image space, in which a change in posipon in I (image) directly projects to a change in posipon in S (which might be a two- or three- dimensional scene). Distances in I (in pixels) correspond to real distances (e.g. in meters) in S. It includes Procedures that operate directly on pixels

6 Filter/ Mask Neighborhood of a point (x,y) can be defined by using a square/ rectangular (common used) or circular subimage area centered at (x,y) (x,y) The center of the subimage is moved from pixel to pixel starpng at the top of the corner

7 Point Processing

8 Point Processing: Contrast Stretching Produces higher contrast than the original by: Darkening the levels below m in the original image Brightening the levels above m in the original image

9 Point Processing: Thresholding funcpon A limipng Case, where the possible outputs includes 0 & 1 only Produce a two- level (binary) image

10 Neighborhood greater than 1x1 pixel Mask Processing/ Filtering Use a funcpon of the values of input Image f in a predefined neighborhood of (x,y) to determine the value of output Image g at (x,y) The value of the mask coefficients determine the nature of the process Used in techniques Image Sharpening Image Smoothing Where R = w 1 z 1 + w 2 z w mn z mn mn = w i z i i=i z i is gray level of pixels associated with mask coefficient w i

11 Basic Gray Level TransformaPons Linear funcpon NegaPve Negative IdenPty transformapons nth root Logarithm funcpon Log Inverse- log transformapon Output gray level, s Log nth power Power- law funcpon n th power Identity Inverse Log n th root transformapons Input gray level, r

12 IdenPty FuncPon Output intensipes are idenpcal to input intensipes Negative nth root Is included in the graph only for completeness The output Image is idenpcal to the Input Image Output gray level, s Log Identity Inverse Log nth power Input gray level, r

13 NegaPve FuncPon Negative nth root Output gray level, s Log nth power Identity Inverse Log Input gray level, r

14 Log FuncPon Negative nth root Output gray level, s Log nth power Identity Inverse Log Input gray level, r

15 Log FuncPon It compresses the dynamic range of images with large variapons in pixel values Example of image with dynamic range: Fourier spectrum image It can have intensity range from 0 to 10 6 or higher We can t see the significant degree of detail as it will be lost in the display Inverse Logarithm Transforma3ons Used to expand the values of high pixels in an image while compressing the darker- level values

16 Example of Logarithm Image Fourier Spectrum with range = 0 to 1.5 x 10 6 Result after apply the log transformation with c = 1, range = 0 to 6.2

17 Power Law TransformaPns Output gray level, s Input gray level, r Used to expand the values of high pixels in an image while compressing the darker-level values.

18 Gamma CorrecPon Monitor Gamma correction γ = 2.5 Monitor γ =1/2.5 = 0.4

19 MRI = Magnetic resonance imaging: Medical imaging technique to visualize internal structures of the body in detail (a) A magnepc resonance image of an upper thoracic human spine with a fracture dislocapon and spinal cord impingement The picture is predominately dark An expansion of gray levels are desirable needs γ < 1 (b) result a9er power- law transformapon with γ = 0.6, c=1 (c) transformapon with γ = 0.4 (best result) (d) transformapon with γ = 0.3 (under acceptable level) Effects of decreasing Gamma When the γ is reduced too much, the image begins to reduce contrast to the point where the image started to have very slight wash-out look, especially in the background Example of MRI a b c d

20 (a) image has a washed- out appearance, it needs a compression of gray levels needs γ > 1 Example a c b d (b) result a9er power- law transformapon with γ = 3.0 (suitable) (c) transformapon with γ = 4.0 (suitable) (d) transformapon with γ = 5.0 (high contrast, the image has areas that are too dark, some detail is lost)

21 Contrast Stretching (a) increase the dynamic range of the gray levels in the image a c b d (b) a low- contrast image : result from poor illuminapon, lack of dynamic range in the imaging sensor (c) result of contrast stretching: (r 1,s 1 ) = (r min,0) and (r 2,s 2 ) = (r max,l- 1) (d) result of thresholding

22 Gray Level Slicing HighlighPng a specific range of gray levels in an image Display a high value of all gray levels in the range of interest and a low value for all other gray levels (a) transformapon highlights range [A,B] of gray level and reduces all others to a constant level (b) transformapon highlights range [A,B] but preserves all other levels a b

23 Bit- plane Slicing HighlighPng the contribupon made to total image appearance by specific bits Suppose each pixel is represented by 8 bits One 8-bit byte Bit-plane 7 (most significant) Higher- order bits contain the majority of the visually significant data Bit-plane 0 (least significant) Useful for analyzing the relapve importance played by each bit of the image

24 Example: Bit- plane Slicing The (binary) image for bit- plane 7 can be obtained by processing the input image with a thresholding gray- level transformapon Map all levels between 0 and 127 to 0 Map all levels between 129 and 255 to 255 An 8-bit fractal image

25 8 Bit- planes Bit- plane 7 Bit- plane 6 Bit- plane 5 Bit- plane 4 Bit- plane 3 Bit- plane 2 Bit- plane 1 Bit- plane 0

26