Spatial Operations

Introduction Fundamentals Image Enhancement Techniques

Speech and Image Processing Lab IMAGE ENHANCEMENT TECHNIQUES Jisa David & Seena Symon M.Tech. Signal Processing

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

INTRODUCTION

DIGITAL IMAGE & IMAGE PROCESSING Image:2D function f(x,y) where x and y are spatial coordinates and f is the intensity. Digital image :Representation of a 2D image as a finite set of digital values( pixels). Digital Image Processing:Processing a digital image by means of a digital computer.

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

FUNDAMENTALS

STEPS IN IMAGE PROCESSING 1 2 3 4 5 6 7

Image Aquisition Image Enhancement Image Restoration Morphological processing Segmentation Representation and Description Recognition

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

IMAGE ENHANCEMENT TECHNIQUES

IMAGE ENHANCEMENT Processing of an image so that the result is more suitable than orignal image for Specific Application. Done in Two domain 1 2

Spatial Domian Frequency Domain

Jisa David & Seena Symon M.Tech. Signal Processing

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Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

SPATIAL DOMAIN

Gray Level Transformation Image Negatives Log Transformations Power-Law transformation Piecewise Linear Transformation Contrast Stretching Gray Level Slicing Bit Plane Slicing

Spatial Filtering Neighborhood Averaging Median Filtering

Histogram processing

Jisa David & Seena Symon M.Tech. Signal Processing

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Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

SPATIAL DOMAIN

GRAY LEVEL TRANSFORMATION Operated on individual pixels intensity values: s = T(r). r: original intensity, s: new intensity Data independent pixel-based enhancement method. Approaches 1

Image negatives

2

Log transform

3

Power law transform

Jisa David & Seena Symon M.Tech. Signal Processing

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Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

GRAY LEVEL TRANSFORMATION IMAGE NEGATIVE Function reverses the order from black to white so that the intensity of the output image decreases as the intensity of the input increases. s= T(r) = L-1-r Used mainly in medical images and to produce slides of the screen

Figure: (a)Original mammogram ,(b)Negative image Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

GRAY LEVEL TRANSFORMATION LOG GRAY LEVEL TRANSFORM s = T(r) = c log(1+r) expand dark value to enhance details of dark area.Example:Fourier 2D Transform.

Figure: (a)Fourier Spectrum ,(b)Transformed image with c=1

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

GRAY LEVEL TRANSFORMATION POWER LAW GRAY-LEVEL TRANSFORM s = T(r) = crγ Gamma correction: To compensate the built-in power law compression due to display characteristics. CRT intensity to voltage response is a power function with γ=1.8-2.5.

Figure: (A)Original Image, (B)Response of monitor to A (C)Gamma Corrected Image,(D)Response of monitor to C

Jisa David & Seena Symon M.Tech. Signal Processing

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Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

GRAY LEVEL TRANSFORMATION

Figure: a) log transformation b)power-law transformation

Jisa David & Seena Symon M.Tech. Signal Processing

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Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

GRAY LEVEL TRANSFORMATION

PIECE-WISE LINEAR GRAY-LEVEL TRANSFORM Allow more control on the complexity of T(r) Examples Contrast stretching Gray-level slicing Bit-plane slicing

Jisa David & Seena Symon M.Tech. Signal Processing

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Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

PIECE-WISE LINEAR GRAY-LEVEL TRANSFORM

CONTRAST STRETCHING To increase the dynamic range of the gray levels in the image being processed. The locations of (r1,s1) and (r2,s2) control the shape of the transformation function. If r1= s1 and r2= s2 the transformation is a linear function and produces no changes. If r1=r2, s1=0 and s2=L-1, the transformation becomes a thresholding function that creates a binary image.

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

PIECE-WISE LINEAR GRAY-LEVEL TRANSFORM CONTRAST STRETCHING

Figure: (A)Transformation function,(B) A low Contrast Image,(C)Result of Contrast Stretching,(D)Result of Thresholding

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

PIECE-WISE LINEAR GRAY-LEVEL TRANSFORM GRAY LEVEL SLICING 1

2

To highlight a specific range of gray levels in an image (e.g. to enhance certain features). The second approach is to brighten the desired range of gray levels but preserve the background and gray-level tonalities in the image

Figure: (a)Transformation function of (1),(b)Transformation function of (2) Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

PIECE-WISE LINEAR GRAY-LEVEL TRANSFORM

Figure: a) original image b)Transformed image

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

PIECE-WISE LINEAR GRAY-LEVEL TRANSFORM

BIT PLANE SLICING To highlight the contribution made to the total image appearance by specific bits. i.e. Assuming that each pixel is represented by 8 bits, the image is composed of 8 1-bit planes. Plane 0 contains the least significant bit and plane 7 contains the most significant bit.

Application in image compression

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

PIECE-WISE LINEAR GRAY-LEVEL TRANSFORM BIT PLANE SLICING

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

SPATIAL FILTERING

Use of spatial masks for image processing (spatial filters) Linear and nonlinear filters Low-pass filters eliminate or attenuate high frequency components in the frequency domain (sharp image details), and result in image blurring. The basic approach is to sum products between the mask coefficients and the intensities of the pixels under the mask at a specific location in the image. R = W1 Z1 + W2 Z2 + ......... + W9 Z9

(1)

(for 3x3 filter)

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

SPATIAL FILTERING

Figure: Mechanism of Spatial Filtering Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

SPATIAL FILTERING

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

SPATIAL FILTERING

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

SPATIAL FILTERING

NEIGHBORHOOD AVERAGING Reduce the noise Averaging using M neighborhood Pixels As M increases, the variability of the pixel values at each location decreases

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

SPATIAL FILTERING NEIGHBORHOOD AVERAGING

Figure: (a)image, (b)Corrupted Image,(c)-(f) result of averaging with M =8,16,64,128 Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

SPATIAL FILTERING MEDIAN FILTERING The gray level of each pixel is replaced by the median of the gray levels in the neighborhood of that pixel (instead of by the average as before). Median filters are nonlinear. Median filtering reduces noise without blurring edges and other sharp details. Median filtering is particularly effective when the noise pattern consists of strong, spike-like components. (Salt-and-pepper noise.)

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

SPATIAL FILTERING

MEDIAN FILTERING

Figure: (a)Image correpted by Salt & Pepper Noise, (b)Noise reduction with 3x3 averaging mask, (c)Noise reduction with 3x3 median filtering.

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

SPATIAL FILTERING EDGE ENHANCEMENT digital image processing filter that improves the apparent sharpness of an image or video Application in TV broad cast and DVD

Jisa David & Seena Symon M.Tech. Signal Processing

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Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

HISTOGRAM PROCESSING Data-dependent pixel-based image enhancement method. The histogram of a digital image with gray levels from 0 to L-1 is a discrete function h(rk )=nk , where: rk is the kth gray level nk is the No: of pixels in the image with that gray level n is the total number of pixels in the image k = 0, 1, 2, , L-1

Normalized histogram: p(rk )=nk /n(an estimate of the probability of occurrence of gray level rk )

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

HISTROGRAM PROCESSING

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

FREQUENCY DOMAIN FILTERING IN FREQUENCY DOMAIN Compute Fourier transform of image Multiply the result by a filter transfer function (or simply filter). Take the inverse transform to produce the enhanced image.

Figure: Basic Step for Filtering in the Frequency Domain Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

FREQUENCY DOMAIN ENHANCEMENT IN FREQUENCY DOMAIN Types of enhancement that can be done: Lowpass filtering: Reduce the high-frequency content – blurring or smoothing Highpass filtering: increase the magnitude of high-frequency components relative to low-frequency components – sharpening.

Figure: (a)Frequency response of Low pass fillter and (b)High pass filter

Jisa David & Seena Symon M.Tech. Signal Processing

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Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

FREQUENCY DOMAIN BUTTERWORTH LOW PASS FILTER This filter does not have a sharp discontinuity establishing a clear cutoff between passed and filtered frequencies

Transfer functiion H(u, v) =

1 1+[D(u,v)/D0 ]2n

where D(u, v) = [(u − M/2)2 + (v − N/2)2 ] where n -order of the filter,M-Number of rows,N -Number of columns,D0 -Cut off frequency

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

FREQUENCY DOMAIN

Figure: a) original image b)Noisy image c)LPF image Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

FREQUENCY DOMAIN BUTTERWORTH HIGH PASS FILTER Edges and sharp transitions in grayvalues in an image contribute significantly to high-frequency content of its Fourier transform. Image sharpening in the Frequency domain can be done by attenuating the low-frequency content of its Fourier transform. Transfer functiion H(u, v) =

1 1+[D0 /D(u,v)]2n

where D(u, v) = [(u − M/2)2 + (v − N/2)2 ] where n -order of the filter,M-Number of rows,N -Number of columns,D0 -Cut off frequency

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

FREQUENCY DOMAIN

Figure: a)Original image b)HPF image

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

FREQUENCY DOMAIN BUTTERWORTH BAND PASS FILTER Band-boost filtering boosts certain midrange freqencies and partially corrects for blurring, but does not boost the very high (most noise corrupted) frequencies. Obtained by cascading low pass and High pass filters. Transfer functiion H(u, v) =

1 (1+[D(u,v)/D1 ]2n )(1+[D0 /D(u,v)]2n )

where D(u, v) = [(u − M/2)2 + (v − N/2)2 ] where n -order of the filter,M-Number of rows,N -Number of columns,D0 - Lower Cut off frequency ,D1 -Upper Cut off frequency

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

FALSE COLORING A false-color image is an image that depicts a subject in colors that differ from human perception of the same subject. A false-color image is derived from a greyscale image by mapping each pixel value to a color according to a table or function. False-color images are frequently used for viewing satellite images, such as from weather satellites.

Figure: (a)Original Image, (b)False Color Image Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME

Introduction Fundamentals Image Enhancement Techniques

Spatial Domain Frequency Domain

REFERENCE

Rafael C. Gonzalez, richard E. Woods,Digital Image Processing,Pearson Education. Anil K. Jain, Fundamentals of Digital Image Processing,Prentice Hall Rafael C. Gonzalez, richard E. Woods,Digital Image Processing using MATLAB,Pearson Education. www.wikipedia.org

Jisa David & Seena Symon M.Tech. Signal Processing

Speech and Image Processing Lab IMAGE ENHANCEME