Image Pre-Processing Techniques and Transformations, Lecture notes of Computer Science

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Image Pre-Processing

Ashish Khare

 (^) Pre-processing is a common name for operations with images at the lowest level of abstraction -- both input and output are intensity images.  (^) The aim of pre-processing is an improvement of the image data that suppresses unwanted distortions or enhances some image features important for further processing.

 (^) Image pre-processing methods use the considerable redundancy in images.  (^) Neighboring pixels corresponding to one object in real images have essentially the same or similar brightness value.  (^) Thus, distorted pixel can often be restored as an average value of neighboring pixels.  (^) Do you remember the example of filtering impulse noise?

 (^) If pre-processing aims to correct some degradation in the image, the nature of a priori information is important:  (^) knowledge about the nature of the degradation; only very general properties of the degradation are assumed.  (^) knowledge about the properties of the image acquisition device, and conditions under which the image was obtained. The nature of noise (usually its spectral characteristics) is sometimes known.  (^) knowledge about objects that are searched for in the image, which may simplify the pre-processing very considerably.

Pixel Brightness Transformations  (^) Brightness transformations modify pixel brightness -- the transformation depends on the properties of a pixel itself.  (^) Brightness corrections  (^) Gray scale transformations.  (^) Brightness correction  (^) considers original brightness  (^) pixel position in the image.  Gray scale transformations  (^) change brightness without regard to position in the image.

Position dependent brightness correction  (^) Ideally, the sensitivity of image acquisition and digitization devices should not depend on position in the image, but this assumption is not valid in many practical cases.  Sources of degradation.  (^) Uneven sensitivity of light sensors  (^) Uneven object illumination  (^) Systematic degradation can be suppressed by brightness correction.

 (^) If a reference image g(i,j) is known (e.g., constant brightness c) then  (^) the degraded result is f c(i,j)  (^) systematic brightness errors can be suppressed:

 (^) Image degradation process must be stable,  (^) the device should be calibrated time to time (find error coefficients e(i,j))  (^) This method implicitly assumes linearity of the transformation, which is not true in reality as the brightness scale is limited into some interval.  (^) overflow is possible  (^) the best reference image has brightness that is far enough from both limits.

Gray Scale Transformations  (^) Grey scale transformations do not depend on the position of the pixel in the image.  (^) Brightness transform

a - Negative transformation b - contrast enhancement (between p and p2) c - Brightness thresholding

 (^) A geometric transform is a vector function T that maps the pixel (x,y) to a new position (x',y').  (^) The transformation equations are either known in advance or can be determined from known original and transformed images.  (^) Several pixels in both images with known correspondence are used to derive the unknown transformation.