21-04-2010, 10:56 AM
[attachment=3275]
IMAGE PROCESSING
PRESENTED BY:
1. T.Krishna Kanth
2.N.V.Ram Kishore
D.V.R. College of Engineering and Technology.
Kandi, Hyderabad
Andhra Pradesh.
ABSTRACT:
An image is digitized to convert it to a form which can be stored in a computer's memory or on some form of storage media such as a hard disk or CD-ROM. This digitization procedure can be done by a scanner, or by a video camera connected to a frame grabber board in a computer. Once the image is digitized, two things are most important it is to be stored or transmitted with minimum bits and it should be restored with maximum clarity .This makes way to the various image processing operations.
Image processing operations are divided into three major categories, Image Compression, Image Enhancement and Restoration, and Measurement Extraction. Image compression is familiar to most people. It involves reducing the amount of memory needed to store a digital image. Where as the Image Enhancement and Restoration deals with the retrieval of the image back.
This paper deals with the Image Enhancement and Restoration which helps in the image restoration with maximum clarity and enhancement of the image quality.
The first section describes what Image Enhancement and Restoration is and the second section tells us about the techniques used for the Image Enhancement and Restoration and, final section describes the advantages and disadvantage of using these techniques.
A Short Introduction to Digital Image Processing
An image is digitized to convert it to a form which can be stored in a computer's memory or on some form of storage media such as a hard disk or CD-ROM. This digitization procedure can be done by a scanner, or by a video camera connected to a frame grabber board in a computer. Once the image has been digitized, it can be operated upon by various image processing operations.
Image processing operations can be roughly divided into three major categories
Image Compression
Image Enhancement and Restoration
Measurement Extraction
Image compression is familiar to most people. It involves reducing the amount of memory needed to store a digital image.
Image defects which could be caused by the digitization process or by faults in the imaging set-up (for example, bad lighting) can be corrected using Image Enhancement techniques. Once the image is in good condition, the Measurement Extraction operations can be used to obtain useful information from the image.
Some examples of Image Enhancement and Measurement Extraction are given below. The examples shown all operate on 256 grey-scale images. This means that each pixel in the image is stored as a number between 0 to 255, where 0 represents a black pixel, 255 represents a white pixel and values in-between represent shades of grey. These operations can be extended to operate on colour images.
The examples below represent only a few of the many techniques available for operating on images. Details about the inner workings of the operations have not been given, but some references to books containing this information are given at the end for the interested reader.
Image Enhancement and Restoration
The image at the left of Figure 1 has been corrupted by noise during the digitization process. The 'clean' image at the right of Figure 1 was obtained by applying a median filter to the image.
Figure 1. Application of the median filter
An image with poor contrast, such as the one at the left of Figure 2, can be improved by adjusting the image histogram to produce the image shown at the right of Figure 2.
Figure 2. Adjusting the image histogram to improve image contrast
The image at the top left of Figure 3 has a corrugated effect due to a fault in the acquisition process. This can be removed by doing a 2-dimensional Fast-Fourier Transform on the image (top right of Figure 3), removing the bright spots (bottom left of Figure 3), and finally doing an inverse Fast Fourier Transform to return to the original image without the corrugated background bottom right of Figure 3).
Figure 3. Application of the 2-dimensional Fast Fourier Transform
An image which has been captured in poor lighting conditions, and shows a continuous change in the background brightness across the image (top left of Figure 4) can be corrected using the following procedure. First remove the foreground objects by applying a 25 by 25 greyscale dilation operation (top right of Figure 4). Then subtract the original image from the background image (bottom left of Figure 4). Finally invert the colors and improve the contrast by adjusting the image histogram (bottom right of Figure 4)
Figure 4. Correcting for a background gradient
Image Measurement Extraction
The example below demonstrates how one could go about extracting measurements from an image. The image at the top left of Figure 5 shows some objects. The aim is to extract information about the distribution of the sizes (visible areas) of the objects. The first step involves segmenting the image to separate the objects of interest from the background. This usually involves thresholding the image, which is done by setting the values of pixels above a certain threshold value to white, and all the others to black (top right of Figure 5). Because the objects touch, thresholding at a level which includes the full surface of all the objects does not show separate objects. This problem is solved by performing a watershed separation on the image (lower left of Figure 5). The image at the lower right of Figure 5 shows the result of performing a logical AND of the two images at the left of Figure 5. This shows the effect that the watershed separation has on touching objects in the original image.
Finally, some measurements can be extracted from the image. Figure 6 is a histogram showing the distribution of the area measurements. The areas were calculated based on the assumption that the width of the image is 28 cm.
Figure 5. Thresholding an image and applying a Watershed Separation Filter
Figure 6. Histogram showing the Area Distribution of the Objects
Basic Enhancement and Restoration Techniques
¢ Unsharp masking
¢ Noise suppression
¢ Distortion suppression
The process of image acquisition frequently leads (inadvertently) to image degradation. Due to mechanical problems, out-of-focus blur, motion, inappropriate illumination, and noise the quality of the digitized image can be inferior to the original. The goal of enhancement is-- starting from a recorded image c[m,n]--to produce the most visually pleasing image â[m,n]. The goal of restoration is--starting from a recorded image c[m,n]--to produce the best possible estimate â[m,n] of the original image a[m,n]. The goal of enhancement is beauty; the goal of restoration is truth.
The measure of success in restoration is usually an error measure between the original a[m,n] and the estimate â[m,n]: E{â[m,n], a[m,n]}. No mathematical error function is known that corresponds to human perceptual assessment of error. The mean-square error function is commonly used because:
1. It is easy to compute;
2. It is differentiable implying that a minimum can be sought;
3. It corresponds to "signal energy" in the total error, and;
4. It has nice properties vis àvis Parseval's theorem, eqs. (22) and (23).
The mean-square error is defined by:
In some techniques an error measure will not be necessary; in others it will be essential for evaluation and comparative purposes.
Unsharp masking
A well-known technique from photography to improve the visual quality of an image is to enhance the edges of the image. The technique is called unsharp masking. Edge enhancement means first isolating the edges in an image, amplifying them, and then adding them back into the image. Examination of Figure 33 shows that the Laplacian is a mechanism for isolating the gray level edges. This leads immediately to the technique:
The term k is the amplifying term and k > 0. The effect of this technique is shown in Figure 48.
The Laplacian used to produce Figure 48 is given by eq. (120) and the amplification term k = 1.
Original Laplacian-enhanced
Figure 48: Edge enhanced compared to original
Noise suppression
The techniques available to suppress noise can be divided into those techniques that are based on temporal information and those that are based on spatial information. By temporal information we mean that a sequence of images {ap[m,n] | p=1,2,...,P} are available that contain exactly the same objects and that differ only in the sense of independent noise realizations. If this is the case and if the noise is additive, then simple averaging of the sequence:
Temporal averaging -
will produce a result where the mean value of each pixel will be unchanged. For each pixel, however, the standard deviation will decrease from to .
If temporal averaging is not possible, then spatial averaging can be used to decrease the noise. This generally occurs, however, at a cost to image sharpness. Four obvious choices for spatial averaging are the smoothing algorithms that have been described in Section 9.4 - Gaussian filtering (eq. (93)), median filtering, Kuwahara filtering, and morphological smoothing (eq. ).
Within the class of linear filters, the optimal filter for restoration in the presence of noise is given by the Wiener filter . The word "optimal" is used here in the sense of minimum mean-square error (mse). Because the square root operation is monotonic increasing, the optimal filter also minimizes the root mean-square error (rms). The Wiener filter is characterized in the Fourier domain and for additive noise that is independent of the signal it is given by:
where Saa(u,v) is the power spectral density of an ensemble of random images {a[m,n]} and Snn(u,v) is the power spectral density of the random noise. If we have a single image then Saa(u,v) = |A(u,v)|2. In practice it is unlikely that the power spectral density of the uncontaminated image will be available. Because many images have a similar power spectral density that can be modeled by Table 4-T.8, that model can be used as an estimate of Saa(u,v).
A comparison of the five different techniques described above is shown in Figure 49. The Wiener filter was constructed directly from eq. because the image spectrum and the noise spectrum were known. The parameters for the other filters were determined choosing that value (either or window size) that led to the minimum rms.
a) Noisy image (SNR=20 dB) b) Wiener filter c) Gauss filter ( = 1.0)
rms = 25.7 rms = 20.2 rms = 21.1
d) Kuwahara filter (5 x 5) e) Median filter (3 x 3) f) Morph. smoothing (3 x 3)
rms = 22.4 rms = 22.6 rms = 26.2
Figure 49: Noise suppression using various filtering techniques.
The root mean-square errors (rms) associated with the various filters are shown in Figure 49. For this specific comparison, the Wiener filter generates a lower error than any of the other procedures that are examined here. The two linear procedures, Wiener filtering and Gaussian filtering, performed slightly better than the three non-linear alternatives.
Distortion suppression
The model presented above--an image distorted solely by noise--is not, in general, sophisticated enough to describe the true nature of distortion in a digital image. A more realistic model includes not only the noise but also a model for the distortion induced by lenses, finite apertures, possible motion of the camera and/or an object, and so forth. One frequently used model is of an image a[m,n] distorted by a linear, shift-invariant system ho[m,n] (such as a lens) and then contaminated by noise [m,n]. Various aspects of ho[m,n] and [m,n] have been discussed in earlier sections. The most common combination of these is the additive model:
The restoration procedure that is based on linear filtering coupled to a minimum mean-square error criterion again produces a Wiener filter :
Once again Saa(u,v) is the power spectral density of an image, Snn(u,v) is the power spectral density of the noise, and o(u,v) = F{ho[m,n]}. Examination of this formula for some extreme cases can be useful. For those frequencies where Saa(u,v) >> Snn(u,v), where the signal spectrum dominates the noise spectrum, the Wiener filter is given by 1/o(u,v), the inverse filter solution. For those frequencies where Saa(u,v) << Snn(u,v), where the noise spectrum dominates the signal spectrum, the Wiener filter is proportional to o*(u,v), the matched filter solution. For those frequencies where o(u,v) = 0, the Wiener filter W(u,v) = 0 preventing overflow.
The Wiener filter is a solution to the restoration problem based upon the hypothesized use of a linear filter and the minimum mean-square (or rms) error criterion. In the example below the image a[m,n] was distorted by a bandpass filter and then white noise was added to achieve an SNR = 30 dB. The results are shown in Figure 50.
a) Distorted, noisy image b) Wiener filter c) Median filter (3 x 3)
rms = 108.4 rms = 40.9 Figure 50: Noise and distortion suppression using the Wiener filter, eq. and the median filter.
The rms after Wiener filtering but before contrast stretching was 108.4; after contrast stretching with eq. (77) the final result as shown in Figure 50b has a mean-square error of 27.8. Using a 3 x 3 median filter as shown in Figure 50c leads to a rms error of 40.9 before contrast stretching and 35.1 after contrast stretching. Although the Wiener filter gives the minimum rms error over the set of all linear filters, the non-linear median filter gives a lower rms error. The operation contrast stretching is itself a non-linear operation. The "visual quality" of the median filtering result is comparable to the Wiener filtering result. This is due in part to periodic artifacts introduced by the linear filter which are visible in Figure 50b.
CONCLUSION:
Audio stream contain extremely valuable data, whose contents is also very rich and diverse. The combination of audio and image techniques, will definitely generate interesting results, and very likely improve the quality of the present analysis
REFEREANCE:
[1] Computer Techniques in Image processing By Andrews 1970
[2] Article in the November issue of the journal , ELECTRONICS TODAY.
[3] Article in the January issue of journal, ELECTRONICS FOR YOU
[4] Digital image restoration By Andrews 1977
[5] Digital image processing By Rafael Gonzalez and Richard Woods 2003
IMAGE PROCESSING
PRESENTED BY:
1. T.Krishna Kanth
2.N.V.Ram Kishore
D.V.R. College of Engineering and Technology.
Kandi, Hyderabad
Andhra Pradesh.
ABSTRACT:
An image is digitized to convert it to a form which can be stored in a computer's memory or on some form of storage media such as a hard disk or CD-ROM. This digitization procedure can be done by a scanner, or by a video camera connected to a frame grabber board in a computer. Once the image is digitized, two things are most important it is to be stored or transmitted with minimum bits and it should be restored with maximum clarity .This makes way to the various image processing operations.
Image processing operations are divided into three major categories, Image Compression, Image Enhancement and Restoration, and Measurement Extraction. Image compression is familiar to most people. It involves reducing the amount of memory needed to store a digital image. Where as the Image Enhancement and Restoration deals with the retrieval of the image back.
This paper deals with the Image Enhancement and Restoration which helps in the image restoration with maximum clarity and enhancement of the image quality.
The first section describes what Image Enhancement and Restoration is and the second section tells us about the techniques used for the Image Enhancement and Restoration and, final section describes the advantages and disadvantage of using these techniques.
A Short Introduction to Digital Image Processing
An image is digitized to convert it to a form which can be stored in a computer's memory or on some form of storage media such as a hard disk or CD-ROM. This digitization procedure can be done by a scanner, or by a video camera connected to a frame grabber board in a computer. Once the image has been digitized, it can be operated upon by various image processing operations.
Image processing operations can be roughly divided into three major categories
Image Compression
Image Enhancement and Restoration
Measurement Extraction
Image compression is familiar to most people. It involves reducing the amount of memory needed to store a digital image.
Image defects which could be caused by the digitization process or by faults in the imaging set-up (for example, bad lighting) can be corrected using Image Enhancement techniques. Once the image is in good condition, the Measurement Extraction operations can be used to obtain useful information from the image.
Some examples of Image Enhancement and Measurement Extraction are given below. The examples shown all operate on 256 grey-scale images. This means that each pixel in the image is stored as a number between 0 to 255, where 0 represents a black pixel, 255 represents a white pixel and values in-between represent shades of grey. These operations can be extended to operate on colour images.
The examples below represent only a few of the many techniques available for operating on images. Details about the inner workings of the operations have not been given, but some references to books containing this information are given at the end for the interested reader.
Image Enhancement and Restoration
The image at the left of Figure 1 has been corrupted by noise during the digitization process. The 'clean' image at the right of Figure 1 was obtained by applying a median filter to the image.
Figure 1. Application of the median filter
An image with poor contrast, such as the one at the left of Figure 2, can be improved by adjusting the image histogram to produce the image shown at the right of Figure 2.
Figure 2. Adjusting the image histogram to improve image contrast
The image at the top left of Figure 3 has a corrugated effect due to a fault in the acquisition process. This can be removed by doing a 2-dimensional Fast-Fourier Transform on the image (top right of Figure 3), removing the bright spots (bottom left of Figure 3), and finally doing an inverse Fast Fourier Transform to return to the original image without the corrugated background bottom right of Figure 3).
Figure 3. Application of the 2-dimensional Fast Fourier Transform
An image which has been captured in poor lighting conditions, and shows a continuous change in the background brightness across the image (top left of Figure 4) can be corrected using the following procedure. First remove the foreground objects by applying a 25 by 25 greyscale dilation operation (top right of Figure 4). Then subtract the original image from the background image (bottom left of Figure 4). Finally invert the colors and improve the contrast by adjusting the image histogram (bottom right of Figure 4)
Figure 4. Correcting for a background gradient
Image Measurement Extraction
The example below demonstrates how one could go about extracting measurements from an image. The image at the top left of Figure 5 shows some objects. The aim is to extract information about the distribution of the sizes (visible areas) of the objects. The first step involves segmenting the image to separate the objects of interest from the background. This usually involves thresholding the image, which is done by setting the values of pixels above a certain threshold value to white, and all the others to black (top right of Figure 5). Because the objects touch, thresholding at a level which includes the full surface of all the objects does not show separate objects. This problem is solved by performing a watershed separation on the image (lower left of Figure 5). The image at the lower right of Figure 5 shows the result of performing a logical AND of the two images at the left of Figure 5. This shows the effect that the watershed separation has on touching objects in the original image.
Finally, some measurements can be extracted from the image. Figure 6 is a histogram showing the distribution of the area measurements. The areas were calculated based on the assumption that the width of the image is 28 cm.
Figure 5. Thresholding an image and applying a Watershed Separation Filter
Figure 6. Histogram showing the Area Distribution of the Objects
Basic Enhancement and Restoration Techniques
¢ Unsharp masking
¢ Noise suppression
¢ Distortion suppression
The process of image acquisition frequently leads (inadvertently) to image degradation. Due to mechanical problems, out-of-focus blur, motion, inappropriate illumination, and noise the quality of the digitized image can be inferior to the original. The goal of enhancement is-- starting from a recorded image c[m,n]--to produce the most visually pleasing image â[m,n]. The goal of restoration is--starting from a recorded image c[m,n]--to produce the best possible estimate â[m,n] of the original image a[m,n]. The goal of enhancement is beauty; the goal of restoration is truth.
The measure of success in restoration is usually an error measure between the original a[m,n] and the estimate â[m,n]: E{â[m,n], a[m,n]}. No mathematical error function is known that corresponds to human perceptual assessment of error. The mean-square error function is commonly used because:
1. It is easy to compute;
2. It is differentiable implying that a minimum can be sought;
3. It corresponds to "signal energy" in the total error, and;
4. It has nice properties vis àvis Parseval's theorem, eqs. (22) and (23).
The mean-square error is defined by:
In some techniques an error measure will not be necessary; in others it will be essential for evaluation and comparative purposes.
Unsharp masking
A well-known technique from photography to improve the visual quality of an image is to enhance the edges of the image. The technique is called unsharp masking. Edge enhancement means first isolating the edges in an image, amplifying them, and then adding them back into the image. Examination of Figure 33 shows that the Laplacian is a mechanism for isolating the gray level edges. This leads immediately to the technique:
The term k is the amplifying term and k > 0. The effect of this technique is shown in Figure 48.
The Laplacian used to produce Figure 48 is given by eq. (120) and the amplification term k = 1.
Original Laplacian-enhanced
Figure 48: Edge enhanced compared to original
Noise suppression
The techniques available to suppress noise can be divided into those techniques that are based on temporal information and those that are based on spatial information. By temporal information we mean that a sequence of images {ap[m,n] | p=1,2,...,P} are available that contain exactly the same objects and that differ only in the sense of independent noise realizations. If this is the case and if the noise is additive, then simple averaging of the sequence:
Temporal averaging -
will produce a result where the mean value of each pixel will be unchanged. For each pixel, however, the standard deviation will decrease from to .
If temporal averaging is not possible, then spatial averaging can be used to decrease the noise. This generally occurs, however, at a cost to image sharpness. Four obvious choices for spatial averaging are the smoothing algorithms that have been described in Section 9.4 - Gaussian filtering (eq. (93)), median filtering, Kuwahara filtering, and morphological smoothing (eq. ).
Within the class of linear filters, the optimal filter for restoration in the presence of noise is given by the Wiener filter . The word "optimal" is used here in the sense of minimum mean-square error (mse). Because the square root operation is monotonic increasing, the optimal filter also minimizes the root mean-square error (rms). The Wiener filter is characterized in the Fourier domain and for additive noise that is independent of the signal it is given by:
where Saa(u,v) is the power spectral density of an ensemble of random images {a[m,n]} and Snn(u,v) is the power spectral density of the random noise. If we have a single image then Saa(u,v) = |A(u,v)|2. In practice it is unlikely that the power spectral density of the uncontaminated image will be available. Because many images have a similar power spectral density that can be modeled by Table 4-T.8, that model can be used as an estimate of Saa(u,v).
A comparison of the five different techniques described above is shown in Figure 49. The Wiener filter was constructed directly from eq. because the image spectrum and the noise spectrum were known. The parameters for the other filters were determined choosing that value (either or window size) that led to the minimum rms.
a) Noisy image (SNR=20 dB) b) Wiener filter c) Gauss filter ( = 1.0)
rms = 25.7 rms = 20.2 rms = 21.1
d) Kuwahara filter (5 x 5) e) Median filter (3 x 3) f) Morph. smoothing (3 x 3)
rms = 22.4 rms = 22.6 rms = 26.2
Figure 49: Noise suppression using various filtering techniques.
The root mean-square errors (rms) associated with the various filters are shown in Figure 49. For this specific comparison, the Wiener filter generates a lower error than any of the other procedures that are examined here. The two linear procedures, Wiener filtering and Gaussian filtering, performed slightly better than the three non-linear alternatives.
Distortion suppression
The model presented above--an image distorted solely by noise--is not, in general, sophisticated enough to describe the true nature of distortion in a digital image. A more realistic model includes not only the noise but also a model for the distortion induced by lenses, finite apertures, possible motion of the camera and/or an object, and so forth. One frequently used model is of an image a[m,n] distorted by a linear, shift-invariant system ho[m,n] (such as a lens) and then contaminated by noise [m,n]. Various aspects of ho[m,n] and [m,n] have been discussed in earlier sections. The most common combination of these is the additive model:
The restoration procedure that is based on linear filtering coupled to a minimum mean-square error criterion again produces a Wiener filter :
Once again Saa(u,v) is the power spectral density of an image, Snn(u,v) is the power spectral density of the noise, and o(u,v) = F{ho[m,n]}. Examination of this formula for some extreme cases can be useful. For those frequencies where Saa(u,v) >> Snn(u,v), where the signal spectrum dominates the noise spectrum, the Wiener filter is given by 1/o(u,v), the inverse filter solution. For those frequencies where Saa(u,v) << Snn(u,v), where the noise spectrum dominates the signal spectrum, the Wiener filter is proportional to o*(u,v), the matched filter solution. For those frequencies where o(u,v) = 0, the Wiener filter W(u,v) = 0 preventing overflow.
The Wiener filter is a solution to the restoration problem based upon the hypothesized use of a linear filter and the minimum mean-square (or rms) error criterion. In the example below the image a[m,n] was distorted by a bandpass filter and then white noise was added to achieve an SNR = 30 dB. The results are shown in Figure 50.
a) Distorted, noisy image b) Wiener filter c) Median filter (3 x 3)
rms = 108.4 rms = 40.9 Figure 50: Noise and distortion suppression using the Wiener filter, eq. and the median filter.
The rms after Wiener filtering but before contrast stretching was 108.4; after contrast stretching with eq. (77) the final result as shown in Figure 50b has a mean-square error of 27.8. Using a 3 x 3 median filter as shown in Figure 50c leads to a rms error of 40.9 before contrast stretching and 35.1 after contrast stretching. Although the Wiener filter gives the minimum rms error over the set of all linear filters, the non-linear median filter gives a lower rms error. The operation contrast stretching is itself a non-linear operation. The "visual quality" of the median filtering result is comparable to the Wiener filtering result. This is due in part to periodic artifacts introduced by the linear filter which are visible in Figure 50b.
CONCLUSION:
Audio stream contain extremely valuable data, whose contents is also very rich and diverse. The combination of audio and image techniques, will definitely generate interesting results, and very likely improve the quality of the present analysis
REFEREANCE:
[1] Computer Techniques in Image processing By Andrews 1970
[2] Article in the November issue of the journal , ELECTRONICS TODAY.
[3] Article in the January issue of journal, ELECTRONICS FOR YOU
[4] Digital image restoration By Andrews 1977
[5] Digital image processing By Rafael Gonzalez and Richard Woods 2003
