DSIP COMP/IT Extra Questions:
1. Explain brightness adaption.
2. Explain Image Acquisition.
3. Explain Pont, line and Edge detection.
4. Explain fundamental steps in image processing.
5. Justify:
Unit step signal is power signal.
Refer notebook
6. If energy of a signal is finite then its power is zero.
take any signal whose energy is finite and then show that its power is zero.
Refer notebook
7. Brightness discrimination is poor at low level of illumination.
Write answer from Q. no. 1
8. Enhancement process does not add any information to the image.
True
The principle objective of enhancement is to process an image so that the result is more suitable than
the original image for a specific application.
In image enhancement, original pixels are modified by using either spatial or frequency domain methods
But it doesn't add any additional information.
e.g.
Give example of image negative.
9. All image compression techniques are invertible.
False.
Generally there are 2 types of compression.
Lossy and Lossless.
In lossy compression, it is not possible to recover original data because in compressed image, some data
lost.
i.e. Lossy compression is non-invertible.
But this compression is generally used in audio, video files.
In lossless compression, it is possible to recover original data because in compressed image, no data lost
i.e. Lossless compression is invertible.
This compression is generally used in to compress legal documents, medical records.
Hence all image compression techniques are not invertible.
Some are invertible and others are non-invertible.
10. Explain point, line and edge detection.
After this Write edge detection operators with their mask .
Refer notebook.
Only For Computer
Q. What are the advantages and disadvantages of Wiener filter?
Advantages:
1. A Wiener filter is better than inverse filter in the presence of noise because it uses a prior
statistical knowledge of the noise field.
2. The transfer function of the Wiener filter is chosen to minimize the mean square error using
statistical information on both image and noise fields.
3. It is a stable filter
4. It also reduces the effect of additive noise and produces best reconstruction in terms of the
mean least square error.
5. The Wiener filtering is applied to the image with a cascade implementation of the noise
smoothing and inverse filtering.
Disadvantages:
1. The power spectra of the undegraded image and noise must be known.
2. Weights all errors equally regardless of their location in the image, while the eye is
considerably more tolerant of errors in the dark areas and high-gradient areas in the image.
3. In minimizing the mean square error, Wiener filter also smooth the image more than the
eye would prefer.
4. It is not suitable to minimize blurring
Q. Image restoration
The purpose of image restoration is to "compensate for" or "undo" defects which degrade an
image. Degradation comes in many forms such as motion blur, noise, and camera misfocus. In
cases like motion blur, it is possible to come up with an very good estimate of the actual blurring
function and "undo" the blur to restore the original image. In cases where the image is corrupted
by noise, the best we may hope to do is to compensate for the degradation it caused. In this
project, we will introduce and implement several of the methods used in the image processing
world to restore images.
There are three different ways of restoring the image.
Inverse Filtering
If we know of or can create a good model of the blurring function that corrupted an image, the
quickest and easiest way to restore that is by inverse filtering. Unfortunately, since the inverse
filter is a form of high pass filer, inverse filtering responds very badly to any noise that is present
in the image because noise tends to be high frequency.
Wiener Filtering
The inverse filtering is a restoration technique for deconvolution, i.e., when the image is blurred
by a known low pass filter, it is possible to recover the image by inverse filtering or generalized
inverse filtering. However, inverse filtering is very sensitive to additive noise. The approach of
reducing one degradation at a time allows us to develop a restoration algorithm for each type of
degradation and simply combine them. The Wiener filtering executes an optimal tradeoff
between inverse filtering and noise smoothing. It removes the additive noise and inverts the
blurring simultaneously.
The Wiener filtering is optimal in terms of the mean square error. In other words, it minimizes
the overall mean square error in the process of inverse filtering and noise smoothing. The Wiener
filtering is a linear estimation of the original image. The approach is based on a stochastic
framework. The orthogonality principle implies that the Wiener filter in Fourier domain can be
expressed as follows:
where
and
are respectively power spectra of the original image and the additive noise,
is the blurring filter. It is easy to see that the Wiener filter has two separate part, an inverse
filtering part and a noise smoothing part. It not only performs the deconvolution by inverse filtering
(highpass filtering) but also removes the noise with a compression operation (lowpass filtering).
Wavelet-based Image Restoration
Although the Wiener filtering is the optimal tradeoff of inverse filtering and noise smoothing, in the case
when the blurring filter is singular, the Wiener filtering actually amplify the noise. This suggests that a
denoising step is needed to remove the amplified noise. Wavelet-based denoising scheme, a successful
approach introduced recently by Donoho, provides a natural technique for this purpose. Therefore, the
image restoration contains two separate steps: Fourier-domain inverse filtering and wavelet-domain
image denoising. The digram is shown as follows.
Donoho's approach for image restoration improves the performance, however, in the case when
the blurring function is not invertible, the algorithm is not applicable. Furthermore, since the two
steps are separate, there is no control over the overall performance of the restoration. Recently,
R. Neelamani et al. proposed a wavelet-based deconvolution technique for ill-conditioned
systems. The idea is simple: employ both Fourier-domain Wiener-like and wavelet-domain
regularization. The regularized inverse filter is introduced by modifying the Wiener filter with a
new-introduced parameter:
The parameter
can be optimally selected to minimize the overall mean-square error.
Q. Image enhancement Vs Image restoration (Write ans in columns)
Image enhancement: “improve” an image subjectively.
Image restoration: remove distortion from image in order to go back to the “original” i.e.
objective process.
Image restoration is different from image enhancement in that the latter is designed to emphasize
features of the image that make the image more pleasing to the observer, but not necessarily to
produce realistic data from a scientific point of view. Image enhancement techniques (like
contrast stretching or de-blurring by a nearest neighbor procedure) provided by "Imaging
packages" use no a priori model of the process that created the image.
With image enhancement noise can be effectively be removed by sacrificing some resolution, but
this is not acceptable in many applications. In a Fluorescence Microscope resolution in the z-
direction is bad as it is. More advanced image processing techniques must be applied to recover
the object.
DeConvolution is an example of image restoration method. It is capable of increasing resolution,
especially in the axial direction Removing noise Increasing contrast