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The correct ANSWER is (d) Power-law TRANSFORMATION

Easiest EXPLANATION: It can be SOLVED by Histogram Specification but it is better handled by Power-law Transformation.

Correct ANSWER is (a) Industrial inspection

For EXPLANATION: GRADIENT is used in Industrial inspection, to aid HUMANS, in detection of defects.

Right answer is (d) All of the Mentioned

The BEST EXPLANATION: All the THREE conditions must be SATISFIED.

The CORRECT ANSWER is (d) NONE of the mentioned

For explanation: Sharpening is achieved using Spatial Differentiation.

Right option is (C) Intensity

The best explanation: The PRINCIPLE OBJECTIVE of SHARPENING, to HIGHLIGHT transitions is Intensity.

Correct choice is (a) First order derivative produces thick edge while SECOND order produces a very fine edge

Easy explanation: the first order derivative remains nonzero along the entire RAMP of constant slope, while the second order derivative REMAIN nonzero only at onset and END of such ramps.

If an edge in an image SHOWS transition like the ramp of constant slope, the first order and second order derivative values shows the production of thick and finer edge respectively.

Correct choice is (c) Both first and second order derivative has the same response

The EXPLANATION: This is because a first order derivative has STRONGER response to a gray-level step than a second order, but, the response BECOMES same if TRANSITION into gray-level step is from ZERO.

Right answer is (B) f(x+1)+ f(x-1)-2f(x)

Best explanation: The definition of a second ORDER DERIVATIVE of a one dimensional image f(x) is:

 (∂^2 f)/∂x^2 =f(x+1)+ f(x-1)-2f(x), where the PARTIAL derivative is used to keep notation same even for f(x, y) when partial derivative will be DEALT along two spatial axes.

Right option is (c) Must be zero along the ramps of CONSTANT SLOPE

Explanation: The second order derivative of a DIGITAL function is defined as:

 Must be zero in the flat areas i.e. areas of constant grey values.

 Must be nonzero at the onset of a gray-level STEP or ramp DISCONTINUITIES.

 Must be zero along the gray-level ramps of constant slope.

Right OPTION is (a) f(x+1)-f(x)

The best I can explain: The definition of a first order derivative of a ONE dimensional image f(x) is:

∂f/∂x= f(x+1)-f(x), where the partial derivative is USED to keep notation same even for f(x, y) when partial derivative will be dealt along TWO SPATIAL axes.

The correct choice is (c) Must be nonzero along the gray-level ramps

The explanation: The FIRST order derivative of a digital FUNCTION is DEFINED as:

 Must be zero in the areas of constant grey values.

 Must be nonzero at the onset of a gray-level step or ramp DISCONTINUITIES.

 Must be nonzero along the gray-level ramps.

Right CHOICE is (B) To SPATIAL differentiation

Easiest EXPLANATION: SMOOTHING is analogous to integration and so, sharpening to spatial differentiation.

The correct CHOICE is (d) All of the mentioned

Easiest explanation: HIGHLIGHTING the fine detail in an image or Enhancing detail that has been blurred because of some error or some NATURAL effect of some METHOD of image acquisition, is the principal objective of SHARPENING spatial filters.

Correct answer is (d) All of the mentioned

Easiest explanation: Derivative operator’s response is proportional to the DISCONTINUITY of the IMAGE at the point where the derivative operation is applied.

Image differentiation enhances edges and discontinuities like NOISE and deemphasizes areas that have SLOWLY varying gray-level values.

Since a sharpening spatial filters are analogous to differentiation, so, all the above mentioned facts are true for sharpening spatial filters.

Correct answer is (C) Second order derivative

Easy explanation: Second order derivatives PRODUCE a double LINE response for the STEP changes in the gray LEVEL. We also note of second-order derivatives that, for similar changes in gray-level values in an image, their response is stronger to a line than to a step, and to a point than to a line.

The CORRECT choice is (c) Thicker

The explanation: We know that, the first order derivative is nonzero along the entire ramp while the second order is ZERO along the ramp. So, we can CONCLUDE that the first order derivatives produce thicker EDGES and the second order derivatives produce much FINER edges.

Right answer is (a) True

Easiest explanation: The point which has very high or very low gray level value COMPARED to its NEIGHBOURS, then that point is called as ISOLATED point or NOISE point. The noise point of is of one pixel size.

Correct option is (a) F(x+1)-f(x)

EASY explanation: The first order DERIVATIVE of a SINGLE dimensional function f(x) is the difference between f(x) and f(x+1).

That is, ∂f/∂x=f(x+1)-f(x).

Correct option is (b) Nonzero response at onset of gray level step

The EXPLANATION is: The derivations of DIGITAL functions are defined in TERMS of DIFFERENCES. The DEFINITION we use for second derivative should be zero in flat segments, zero at the onset of a gray level step or ramp and nonzero along the ramps.

Correct choice is (d) Slow varying GRAY values

The explanation is: We are INTERESTED in the behaviour of derivatives used in sharpening in the constant gray LEVEL areas i.e., flat segments, and at the ONSET and end of DISCONTINUITIES, i.e., step and ramp discontinuities.

The CORRECT choice is (a) True

Best explanation: Fundamentally, the strength of the response of the derivative operative is proportional to the degree of discontinuity in the image. So, we can state that image differentiation enhances the edges, DISCONTINUITIES and deemphasizes the pixels with slow varying GRAY LEVELS.

The correct option is (a) True

For explanation I would say: The APPLICATIONS of image sharpening is present in VARIOUS fields like electronic PRINTING, autonomous guidance in military SYSTEMS, medical imaging and industrial inspection.

Right choice is (d) Differentiation

For explanation I would say: We KNOW that, in blurring the IMAGE, we PERFORM the average of pixels which can be considered as integration. As sharpening is the opposite PROCESS of blurring, logically we can tell that we perform differentiation on the pixels to sharpen the image.

Correct choice is (B) Highlighting the CONTRIBUTION made to total image appearance by SPECIFIC BITS

The best I can explain: 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.

Right choice is (a) Gray-level slicing

For explanation I would say: 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.

The correct choice is (d) The TRANSFORMATION function is SINGLE valued and monotonically increasing

The best I can explain: The locations of points (R1,s1) and (R2,s2) control the shape of the transformation function. If r1≤r2and s1≤s2 then the function is single valued and monotonically increasing.

Right choice is (c) The transformation BECOMES a thresholding FUNCTION that creates a binary IMAGE

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

Right answer is (b) The transformation is a linear function that PRODUCES no changes in GRAY levels

To elaborate: 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.

The correct answer is (C) Piece-wise transformation

The 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.

Right option is (c) Gamma correction

For 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.

The correct OPTION is (a) s=cr^γ

The BEST I can explain: Power-law transformations have the basic form: s=cr^γ where c and g are positive constants. SOMETIMES s=cr^γ is WRITTEN as s=c.(r+ε)^γ to account for an offset (that is, a measurable output when the input is zero).

Right choice is (c) s=L-1-r

The best 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.

Right ANSWER is (b) Power law, LOGARITHMIC and inverse law

To explain: 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.

Correct CHOICE is (B) n^2/2

Explanation: Isolated clusters of pixels that are LIGHT or dark with respect to their neighbours, and WHOSE area is less than n2/2, i.e., half the area of the filter, can be eliminated by using an n×n median filter.

Right CHOICE is (a) g(X,y)=T[f(x,y)]

For explanation I would say: 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.