Lecture 2 - Image Formation: Enhancements

Question 1

What are the primary components of a digital image formation system?

Answer 1

Light source, object, lens, image sensor, and image processor.

Question 2

Define “sampling” in the context of digital image formation.

Answer 2

Sampling is the process of converting a continuous image signal into a discrete signal by measuring the image intensity at regular intervals (pixels).

Question 3

Explain the concept of “quantization” in image processing.

Answer 3

Quantization is the process of mapping a large set of input values to a smaller set, such as rounding off pixel intensity values to the nearest integer in digital images.

Question 4

What is the difference between CCD and CMOS camera technologies?

Answer 4

CCD (Charge-Coupled Device) and CMOS (Complementary Metal Oxide Semiconductor) are two types of image sensors. CCD sensors are known for their high image quality and low noise, while CMOS sensors are typically more power-efficient and faster.

Question 5

Describe the effect of sampling on image distortion.

Answer 5

Coarse sampling can lead to loss of detail in images, making edges and corners less recognizable, whereas fine sampling preserves more details but requires more storage and processing power.

Question 6

Explain the concept of “aliasing” in the context of image sampling.

Answer 6

Aliasing occurs when a signal is undersampled, causing different signals to become indistinguishable (or aliasing into each other), resulting in artifacts such as moiré patterns in images.

Question 7

Define “low-pass filtering” and its purpose in noise suppression.

Answer 7

Low-pass filtering allows low-frequency signals to pass while attenuating high-frequency noise, thereby smoothing the image and reducing noise.

Question 8

What is “median filtering” and its advantage in noise suppression?

Answer 8

Median filtering is a non-linear process that replaces each pixel value with the median value of the intensities in its neighborhood, effectively removing noise while preserving edges.

Question 9

Describe the process of “deblurring” an image.

Answer 9

Deblurring involves techniques such as inverse filtering or Wiener filtering to reverse the effects of blurring in an image, often caused by camera shake or motion.

Question 10

What is “histogram equalization” in contrast enhancement?

Answer 10

Histogram equalization is a technique that adjusts the contrast of an image by redistributing the intensity values so that they span the entire range of possible values, leading to a more uniform histogram.

Question 11

Explain the principle of “anisotropic diffusion” for image enhancement.

Answer 11

Anisotropic diffusion is a process that smooths images while preserving edges by performing Gaussian smoothing within regions of homogeneous intensity and avoiding smoothing across edges.

Question 12

What are the effects of under-quantization on image quality?

Answer 12

Under-quantization reduces the number of intensity levels in an image, leading to visible banding and loss of smooth gradients, negatively affecting the perceptual quality.

Question 13

Write the formula for the Fourier Transform used in image processing.

Answer 13

Question 14

Provide the formula for the Inverse Fourier Transform.

Answer 14

Question 15

What is the convolution theorem in the context of Fourier transforms?

Answer 15

Question 16

Write the formula for the Wiener filter used in deblurring.

Answer 16

Question 17

How does increasing the bit depth of an image affect its quality?

Answer 17

Increasing the bit depth improves the dynamic range and reduces quantization noise, leading to better representation of subtle intensity variations and overall image quality.

Question 18

What are the main differences between Gaussian smoothing and median filtering?

Answer 18

Gaussian smoothing is a linear filter that reduces noise by averaging pixel values with a Gaussian-weighted kernel, while median filtering is a non-linear filter that replaces each pixel value with the median value in its neighborhood, better preserving edges.

Question 19

Explain the role of the Point Spread Function (PSF) in image blurring.

Answer 19

The PSF describes the response of an imaging system to a point source or point object. It characterizes how the system blurs the image, and is used in deblurring algorithms to reverse the blurring effect.

Question 20

Describe the process and benefit of using anisotropic diffusion in image enhancement.

Answer 20

Anisotropic diffusion enhances images by smoothing within regions of similar intensity while preserving edges, thus reducing noise and maintaining important structural details.

Question 21

Why is it important to avoid aliasing in the sampling process, and how can it be achieved?

Answer 21

Aliasing causes distortions and artifacts in the sampled image. It can be avoided by ensuring the sampling rate is high enough (above the Nyquist rate) and by using anti-aliasing filters to remove high-frequency content before sampling.

Question 22

What is the significance of the Modulation Transfer Function (MTF) in image processing?

Answer 22

The MTF measures the ability of an imaging system to reproduce (transfer) various levels of detail (spatial frequencies) from the object to the image. It is crucial for understanding and improving image sharpness and clarity.

Question 23

How does histogram equalization improve image contrast?

Answer 23

Histogram equalization redistributes the intensity values of an image to span the entire available range, thus enhancing the contrast and making features more distinguishable.

Question 24

What is the impact of using a non-local means filter for noise reduction?

Answer 24

Non-local means filtering reduces noise by averaging pixel values based on the similarity of their neighborhoods, preserving textures and details better than traditional local averaging methods.

Question 25

Explain the principle of unsharp masking and its use in image enhancement.

Answer 25

Unsharp masking enhances image sharpness by subtracting a blurred version of the image from the original, emphasizing edges and fine details.

Question 26

How does the choice of filter size affect the results of Gaussian smoothing?

Answer 26

Larger filter sizes result in more smoothing, reducing noise but also potentially blurring important details. Smaller filter sizes preserve details but may not adequately suppress noise.

Question 27

Describe the trade-off between noise suppression and detail preservation in image enhancement.

Answer 27

Effective noise suppression often results in loss of fine details, while preserving details may leave some noise in the image. The goal is to find a balance that minimizes noise without significantly degrading important image features.

Question 28

What are the challenges associated with inverse filtering in image deblurring?

Answer 28

Inverse filtering can amplify noise and is sensitive to errors in the estimated degradation function. It often requires regularization techniques to stabilize the solution and reduce artifacts.