Explanation: Spatial domain processes will be denoted by the expression g(x,y)=T[f(x,y)], where f(x,y) is the input image, g(x,y) is the processed image, and T is an operator on f, defined over some neighborhood of (x, y). In addition, T can operate on a set of input images, such as performing the pixel-by-pixel sum of K images for noise reduction.

Explanation: In introduction to gray-level transformations, which shows three basic types of functions used frequently for image enhancement: linear (negative and identity transformations), logarithmic (log and inverse-log transformations), and power-law (nth power and nth root transformations).The identity function is the trivial case in which output intensities are identical to input intensities. It is included in the graph only for completeness.

Explanation: The negative of an image with gray levels in the range[0,L-1] is obtained by using the negative transformation, which is given by the expression: s=L-1-r.

Explanation: The general form of the log transformation: s=clog10(1+r), where c is a constant, and it is assumed that r ≥ 0.

Explanation: Power-law transformations have the basic form: s=crγ where c and g are positive constants. Sometimes s=crγ is written as s=c.(r+ε)γ to account for an offset (that is, a measurable output when the input is zero).

Explanation: A variety of devices used for image capture, printing, and display respond according to a power law. By convention, the exponent in the power-law equation is referred to as gamma .The process used to correct these power-law response phenomena is called gamma correction.

Explanation: The practical implementation of some important transformations can be formulated only as piecewise functions. The principal disadvantage of piecewise functions is that their specification requires considerably more user input.

Explanation: The locations of points (r1,s1) and (r2,s2) control the shape of the transformation function. If r1=s1 and r2=s2 then the transformation is a linear function that produces no changes in gray levels.

Explanation: If r1=r2, s1=0 and s2=L-1,the transformation becomes a thresholding function that creates a binary image.

Explanation: The locations of points (r1,s1) and (r2,s2) control the shape of the transformation function. If r1≤r2 and s1≤s2 then the function is single valued and monotonically increasing.

Explanation: Highlighting a specific range of gray levels in an image often is desired in gray-level slicing. Applications include enhancing features such as masses of water in satellite imagery and enhancing flaws in X-ray images.

Explanation: Instead of highlighting gray-level ranges, highlighting the contribution made to total image appearance by specific bits might be desired. Suppose , each pixel in an image is represented by 8 bits. Imagine that the image is composed of eight 1-bit planes, ranging from bit-plane 0 for the least significant bit to bit-plane 7 for the most significant bit. In terms of 8-bit bytes, plane 0 contains all the lowest order bits in the bytes comprising the pixels in the image and plane 7 contains all the high-order bits.