Image Restoration: Enhance Your Images with Expert Techniques

Question 1

Which of the following is a fundamental step in image restoration?

Accepted Answer

Noise Removal

Answer

Image Segmentation

Answer

Image Enhancement

Answer

Object Detection

Question 2

What is the primary goal of image denoising?

Accepted Answer

To eliminate unwanted noise from an image

Answer

To enhance image contrast

Answer

To reduce image artifacts

Answer

To improve image sharpness

Question 3

Which type of filter is commonly used for image smoothing?

Accepted Answer

Gaussian Filter

Answer

Canny Filter

Answer

Laplacian Filter

Answer

Sobel Filter

Question 4

What is the aim of image deconvolution in image restoration?

Accepted Answer

To reverse the effects of image degradation

Answer

To enhance image contrast

Answer

To reduce noise in an image

Answer

To sharpen image edges

Question 5

What is the main distinction between image restoration and image enhancement?

Accepted Answer

Image restoration aims to recover the original image, while image enhancement improves the visual quality of the image.

Answer

Image restoration is more complex than image enhancement.

Question 6

What is the purpose of utilizing a regularization term in image restoration?

Accepted Answer

To prevent overfitting and enhance generalization

Answer

To speed up computation time

Answer

To enhance image edges

Answer

To increase noise removal

Question 7

Which of the following is NOT a type of image degradation?

Accepted Answer

Image enhancement

Answer

Blur

Answer

Noise

Answer

Artifacts

Question 8

What is the primary objective of image restoration?

Accepted Answer

To recover degraded images to their original condition

Answer

To increase image contrast

Answer

To decrease image file size

Answer

To generate new images from scratch

Question 9

Which of these is a spatial domain technique for image restoration?

Accepted Answer

Median filter

Answer

Fourier transform

Answer

Wavelet transform

Answer

Wiener filter

Question 10

Which of the following is a typical application of image restoration?

Accepted Answer

Medical Imaging

Answer

Gaming

Answer

Social media

Answer

Web design

Question 11

What is the Wiener filter characterized as?

Accepted Answer

A frequency domain filter that minimizes mean squared error

Answer

A nonlinear filter that preserves edges

Answer

A spatial domain filter that uses a moving average

Question 12

What is the benefit of using a wavelet transform for image restoration?

Accepted Answer

It can represent images in a multi-resolution format

Answer

It is more accurate than the Fourier transform

Answer

It is faster than the Fourier transform

Question 13

Which of the following noise models exhibits random variations in pixel values, commonly found in digital images?

Accepted Answer

Additive White Gaussian Noise (AWGN)

Answer

Poisson Noise

Answer

Speckle Noise

Answer

Salt and Pepper Noise

Question 14

What is the fundamental goal of image restoration techniques?

Accepted Answer

To enhance image quality and clarity by removing distortions, noise, and artifacts.

Answer

To reduce the size of images for efficient storage and transmission.

Answer

To generate new images from scratch.

Question 15

In spatial filtering, which technique calculates a new pixel value as the average of its neighboring pixels?

Accepted Answer

Mean Filter

Answer

Wiener Filter

Answer

Median Filter

Answer

Gaussian Filter

Question 16

What is the primary distinction between linear and nonlinear filters in image restoration?

Accepted Answer

Linear filters preserve the weighted average of pixel values, while nonlinear filters enable more complex operations.

Answer

Nonlinear filters are computationally faster than linear filters.

Question 17

Which frequency domain filtering technique is commonly employed to eliminate periodic noise in images?

Accepted Answer

Fourier Transform

Answer

Wavelet Transform

Answer

Laplacian Transform

Question 18

What advantage does a Wiener filter offer over a Mean filter in image denoising?

Accepted Answer

Wiener filters leverage local image statistics for enhanced noise removal while preserving details.

Answer

Mean filters are computationally more efficient than Wiener filters.

Question 19

Which image restoration technique employs an iterative approach to minimize an energy function?

Accepted Answer

Richardson-Lucy Deconvolution

Answer

Homomorphic Filtering

Answer

Adaptive Histogram Equalization

Answer

Wiener Filter

Question 20

What is a potential drawback of using a Tikhonov regularization term in image restoration?

Accepted Answer

It may result in over-smoothing, leading to loss of image sharpness.

Answer

It is computationally expensive.

Answer

It is sensitive to noise in the input image.

Question 21

Which of the following is NOT a commonly used metric for evaluating the performance of image restoration algorithms?

Accepted Answer

Compression Ratio

Answer

Structural Similarity Index Measure (SSIM)

Answer

Mean Squared Error (MSE)

Answer

Peak Signal-to-Noise Ratio (PSNR)

Question 22

What is the significance of "blind image restoration" in practical applications?

Accepted Answer

It enables image restoration without requiring prior knowledge of the degradation process.

Answer

It is faster and more efficient than traditional image restoration techniques.

Answer

It is more effective at removing noise from images.

Question 23

What is the primary purpose of using a Wiener filter in image restoration?

Accepted Answer

To suppress noise while preserving fine details and edges in the image

Answer

To reduce the appearance of salt and pepper noise

Answer

To enhance image contrast

Answer

To correct for severe geometric distortions

Question 24

Which image restoration technique involves estimating missing or corrupted pixels based on the values of neighboring pixels?

Accepted Answer

Interpolation

Answer

Median filtering

Answer

Histogram equalization

Answer

Back-projection

Question 25

What type of noise is characterized by random variations in pixel values that follow a normal distribution?

Accepted Answer

Gaussian noise

Answer

Impulse noise

Answer

Shot noise

Answer

Speckle noise

Question 26

Which of the following image restoration algorithms updates its estimates iteratively, refining the restored image with each iteration?

Accepted Answer

Richardson-Lucy deconvolution

Answer

Regularized inversion

Answer

Weiner filtering

Answer

Bilateral filtering

Question 27

What is the primary goal of histogram equalization in image restoration?

Accepted Answer

To improve the contrast and visibility of image details by distributing pixel values more evenly

Answer

To remove noise by averaging pixel values

Answer

To reduce artifacts by smoothing the image

Question 28

Which of the following parameters is crucial for accurate deconvolution in image restoration algorithms?

Accepted Answer

Point spread function

Answer

Iteration count

Answer

Noise level

Answer

Image resolution

Question 29

What is a key advantage of using a bilateral filter for image denoising?

Accepted Answer

It effectively preserves edges while smoothing noise, leading to visually pleasing results

Answer

It handles large-scale distortions very well

Answer

It is particularly effective in removing salt and pepper noise

Answer

It is exceptionally fast and computationally efficient

Question 30

Which of the following is **not** a common type of image distortion?

Accepted Answer

Rotation

Answer

Quantization

Answer

Noise

Answer

Blur

Question 31

What is the primary **purpose** of image restoration?

Accepted Answer

To enhance image quality by removing distortions and noise.

Answer

To compress images for efficient storage.

Answer

To create new images from existing ones.

Question 32

Which of the following is a common image **noise** removal technique?

Accepted Answer

Median filter

Answer

Canny edge detector

Answer

Histogram equalization

Answer

Sobel filter

Question 33

What is the **main goal** of image deblurring?

Accepted Answer

To restore sharp edges and details in blurred images.

Answer

To reduce noise and artifacts.

Answer

To enhance image contrast.

Question 34

What is the **role** of regularization in image restoration?

Accepted Answer

To prevent overfitting and preserve image structures.

Answer

To enhance image sharpness.

Answer

To speed up the restoration process.

Question 35

What is the **fundamental assumption** made in many image restoration models?

Accepted Answer

Images follow a specific statistical distribution.

Answer

Images are perfectly aligned and registered.

Answer

Images are free from noise and distortions.

Answer

Images have sharp edges and high contrast.

Question 36

Which of the following is **NOT** considered a distortion in image processing?

Accepted Answer

Color enhancement

Answer

Noise

Answer

Gaussian blur

Answer

Motion blur

Question 37

What is the primary objective of image denoising in image restoration?

Accepted Answer

To remove unwanted noise and artifacts from images

Answer

To eliminate motion blur

Answer

To correct color imbalances

Answer

To sharpen image details

Question 38

Among the following image denoising filters, which one is commonly employed?

Accepted Answer

Median filter

Answer

Gaussian filter

Answer

Sobel filter

Answer

Laplacian filter

Question 39

What is the fundamental distinction between spatial and frequency domain image restoration approaches?

Accepted Answer

Spatial techniques operate on pixel values, while frequency domain techniques analyze the image's frequency spectrum.

Answer

Spatial techniques are always more efficient than frequency domain techniques.

Question 40

What is the primary aim of image deblurring in image restoration?

Accepted Answer

To restore images degraded by camera shake or motion blur

Answer

To reduce image noise

Answer

To enhance image contrast

Question 41

What is the main goal of image super-resolution in image restoration?

Accepted Answer

To improve the resolution of low-resolution images

Answer

To remove motion blur

Answer

To reduce image noise

Answer

To correct color distortions

Question 42

Which of the following approaches is commonly used for image super-resolution?

Accepted Answer

Deep learning-based methods

Answer

Nearest-neighbor interpolation

Answer

Bicubic interpolation

Answer

Bilinear interpolation

Question 43

Which of the following is NOT considered a fundamental step in image restoration?

Accepted Answer

Image enhancement

Answer

Deblurring

Answer

Noise removal

Answer

Artifact suppression

Question 44

Which noise model is characterized by noise pixels being independent and identically distributed with a normal distribution?

Accepted Answer

Gaussian noise

Answer

Poisson noise

Answer

Salt-and-pepper noise

Answer

Speckle noise

Question 45

What is the primary goal of Wiener filtering in image restoration?

Accepted Answer

To minimize the mean squared error between the restored image and the original image.

Answer

To enhance the edges of the restored image.

Answer

To maximize the signal-to-noise ratio of the restored image.

Question 46

What is the key advantage of using a blind deconvolution algorithm for image restoration?

Accepted Answer

It does not require prior knowledge of the blur kernel.

Answer

It is less susceptible to noise compared to other methods.

Answer

It is computationally more efficient than other methods.

Answer

It consistently produces sharper restored images than other methods.

Question 47

What is the primary function of a guided filter in image restoration?

Accepted Answer

To transfer information from a guidance image to the restored image.

Answer

To reduce noise in the restored image.

Answer

To enhance the edges of the restored image.

Question 48

Which of the following image restoration techniques is particularly well-suited for removing motion blur?

Accepted Answer

Richardson-Lucy deconvolution

Answer

Total variation regularization

Answer

Wiener filtering

Answer

Tikhonov regularization

Question 49

What is a major limitation of deep learning-based approaches for image restoration?

Accepted Answer

They require a substantial amount of training data.

Answer

They are computationally expensive.

Answer

Their accuracy is often lower than that of traditional restoration techniques.

Answer

They are prone to overfitting, especially with limited data.

Question 50

Which noise model is characterized by noise values having an equal probability of being positive or negative?

Accepted Answer

Additive White Gaussian Noise

Answer

Speckle Noise

Answer

Poisson Noise

Answer

Salt and Pepper Noise

Question 51

What is the primary role of regularization in image restoration?

Accepted Answer

To prevent overfitting and preserve important image structures.

Answer

To enhance image sharpness.

Answer

To reduce computational time.

Question 52

What is the fundamental assumption of the homomorphic filtering approach to image restoration?

Accepted Answer

The illumination and reflectance components of the image can be effectively separated in the frequency domain.

Answer

The image is completely noise-free.

Answer

The image has uniform illumination.

Question 53

Which of the following is an advantage of using deep learning for image restoration?

Accepted Answer

Can learn complex restoration models from data

Answer

Always produces perfect restoration

Answer

Requires less computational power

Answer

Easy to implement

Question 54

What is a key challenge in evaluating the performance of image restoration algorithms?

Accepted Answer

Lack of ground truth images

Answer

Computational complexity

Answer

Noise in the input images