Skip to main content

Question answer of Image Processing

 

UNIT 1: Image Representation and Modeling

Q1: Explain the concept of digital image representation in detail.

  • Answer:
    A digital image is a two-dimensional function that represents a physical object or scene. It is essentially a matrix where each element (pixel) contains intensity or color information. The size of the image is defined by its resolution (width × height), and each pixel has an intensity or color value.

    • Pixel: The smallest unit of a digital image, typically represented as a square or rectangular cell. Each pixel has a value corresponding to its color or intensity.

    • Resolution: Refers to the number of pixels in the image, which defines the level of detail. Higher resolution means more pixels and finer details.

    • Color Models: Digital images can be grayscale (single intensity) or color (combining three channels for Red, Green, and Blue). Examples include RGB, CMYK, and YCbCr.

    Digital images are obtained by sampling and quantizing a continuous signal. Sampling involves selecting discrete points in a continuous space (like measuring the intensity of light at regular intervals). Quantization converts these values into a finite set of intensity levels.

Q2: What are point operations in image processing? Explain with examples.

  • Answer:
    Point operations are those transformations where each pixel in the image is processed individually without regard to its neighboring pixels. These operations are applied to enhance the image or to extract important features. Point operations are simple and fast because they involve only individual pixel values.

    • Examples of point operations:

      • Brightness Adjustment: This operation adds or subtracts a constant value from each pixel’s intensity. It makes the image either brighter or darker.

      • Contrast Stretching: This increases the contrast in an image by stretching the range of intensity levels to cover the full dynamic range.

      • Thresholding: This converts an image to binary by comparing each pixel to a threshold value. Pixels above the threshold are set to 1 (white), and pixels below are set to 0 (black).

      • Image Negative: This operation inverts the pixel intensity values. If the original intensity is II, the negative intensity will be 255−I255 - I for an 8-bit image.

UNIT 2: Image Quantization and Image Transforms

Q1: What is the sampling theorem? Explain with its application in image processing.

  • Answer:
    The sampling theorem (also known as the Nyquist-Shannon Sampling Theorem) states that a continuous signal can be completely represented by its samples, and the original signal can be reconstructed from the samples if the signal is sampled at a rate greater than twice its highest frequency. This rate is called the Nyquist rate.

    In image processing, this theorem ensures that when converting a continuous image (analog image) to a digital format, the image is sampled sufficiently to retain all the important details. If an image is undersampled (below the Nyquist rate), aliasing occurs, leading to distortion and loss of information.

    • Example: A digital camera sensor captures light in pixels, each corresponding to a sample of the image. If the camera’s sampling rate (resolution) is too low, the captured image will appear jagged or blurry due to aliasing.

Q2: What is Discrete Fourier Transform (DFT)? Discuss its properties.

  • Answer:
    The Discrete Fourier Transform (DFT) is a mathematical transformation used to analyze the frequency content of a discrete signal or image. It converts an image from the spatial domain (pixel intensity) to the frequency domain (sinusoidal components). This transformation is especially useful in image filtering, compression, and analysis.

    The DFT is defined as:

    X(k)=∑n=0N−1x(n)e−j2πNknX(k) = \sum_{n=0}^{N-1} x(n) e^{-j \frac{2\pi}{N}kn}

    where x(n)x(n) is the input signal, X(k)X(k) is the frequency spectrum, and NN is the number of samples.

    • Properties of DFT:

      1. Linearity: The DFT of a sum of two signals is equal to the sum of their DFTs.

      2. Symmetry: The magnitude of the DFT coefficients is symmetric around the midpoint, and the phase is antisymmetric.

      3. Periodicity: The DFT is periodic, with a period equal to the number of samples NN.

      4. Convolution Theorem: The DFT of the convolution of two signals is the product of their individual DFTs.

      5. Parseval’s Theorem: The total energy in the spatial domain is equal to the total energy in the frequency domain.

    The DFT is widely used in image processing tasks such as noise removal, image filtering, and compression.

UNIT 3: Image Enhancement

Q1: What is histogram equalization? Explain the process and its purpose.

  • Answer:
    Histogram equalization is a method used to improve the contrast of an image. It works by redistributing the intensity levels in an image so that the histogram of the output image is uniformly spread across all intensity levels.

    Process:

    1. Compute the histogram of the input image.

    2. Calculate the cumulative distribution function (CDF) of the histogram.

    3. Normalize the CDF to cover the range of intensity levels.

    4. Map the old pixel values to new values using the CDF.

    The purpose of histogram equalization is to enhance images that are poorly contrasted (e.g., images with narrow intensity range). It helps in revealing hidden details in dark or bright areas of the image. It is especially useful in medical imaging, satellite imagery, and low-light photography.

Q2: Explain the concept of multi-spectral image enhancement.

  • Answer:
    Multi-spectral image enhancement involves improving the quality of images captured across multiple spectral bands, such as infrared, visible light, and ultraviolet. These images are typically used in satellite and remote sensing applications.

    • Methods for Enhancement:

      1. Contrast Enhancement: Stretching or equalizing the histogram of individual spectral bands.

      2. Principal Component Analysis (PCA): A technique to reduce dimensionality and enhance key features by analyzing the variance in spectral bands.

      3. Filtering: Spatial filtering (e.g., median, Gaussian) is applied to multi-spectral images to remove noise and enhance edges.

    Multi-spectral image enhancement improves image quality for better analysis, classification, and object detection, especially in remote sensing applications where different spectral bands carry different information about the environment.

UNIT 4: Image Restoration

Q1: Explain Wiener filtering and its application in image restoration.

  • Answer:
    Wiener filtering is a method used for noise reduction and image restoration. It works by minimizing the mean square error between the restored and the true image. Wiener filtering assumes that both the signal and noise have known statistical properties (mean and variance).

    The Wiener filter equation is:

    H(u,v)=S(u,v)S(u,v)+N(u,v)H(u,v) = \frac{S(u,v)}{S(u,v) + N(u,v)}

    where S(u,v)S(u,v) is the power spectral density of the signal and N(u,v)N(u,v) is the power spectral density of the noise.

    Application: Wiener filtering is commonly applied in restoring images corrupted by Gaussian noise or blur. It is used in medical imaging, satellite image restoration, and low-light photography to reduce noise while retaining important features.

Q2: What is blind deconvolution? Explain its use in image restoration.

  • Answer:
    Blind deconvolution is a technique used in image restoration when the blur function is unknown. In traditional deconvolution, the blur is known, and the original image can be recovered by reversing the effects of the blur. However, in blind deconvolution, both the original image and the blur kernel are estimated simultaneously.

    Process: The method involves iterating between estimating the blurred image and the blur kernel until a stable solution is found.

    Applications:

    • Used when the degradation of an image is due to unknown blur, such as motion blur.

    • It is commonly applied in situations where capturing the exact conditions of the image is impossible, like in security cameras or low-quality images.

UNIT 5: Data Compression

Q1: Explain the difference between lossless and lossy compression techniques.

  • Answer:

    • Lossless Compression: This technique compresses the data without losing any information. The original image can be perfectly reconstructed from the compressed data. Examples include PNG and TIFF formats.

    • Lossy Compression: This technique discards some of the image data to reduce file size. The quality of the reconstructed image is slightly degraded. Examples include JPEG and MP3.

    Need for Lossy Compression: Lossy compression is preferred when file size is the primary concern, such as in web images and video streaming, where a slight loss in quality is acceptable.

Q2: What is predictive coding in image compression?

  • Answer:
    Predictive coding is a method where the value of a pixel is predicted based on neighboring pixel values, and only the difference (or residual) between the predicted and actual value is stored. This reduces the amount of data required to represent the image.

    Example: In video compression, the difference between consecutive frames is often much smaller than the full frame itself, so only the difference is encoded, leading to high compression efficiency.

Labels:

Comments

Post a Comment

Popular posts from this blog

Raster scan Vs Vector Scan

1. Raster Scan Display   How It Works : A raster scan display works by painting an image on the screen pixel by pixel, row by row. It follows a systematic pattern where the electron beam (in CRT monitors) or the display elements (in modern LCD/LED screens) sweep across the screen from left to right, top to bottom, in a series of horizontal lines (scan lines). This process is akin to how a traditional TV screen works.   Process : The display draws the image starting from the top-left corner, moving to the right, then moves to the next row below, and repeats this process until the entire screen is filled. This pattern creates a grid of pixels, where each pixel can have a color and brightness level.   Characteristics : Pixel-based : The screen consists of a grid of pixels, and each pixel can have a distinct color and intensity. Continuous Image : Raster scan displays are capable of displaying detailed and complex images, including photographs and videos, because they break t...
Post a Comment
Read more

Inheritance

*■ Inheritance*  • Inheritance is a concept in OOP that allows a class to inherit properties and behaviors (methods) from another class. • A class that inherits from another class is called a derived class (or subclass) • The class which gets inherited by another class is called the base class (or superclass). • Inheritance is possible only if there is is-a relationship between parent and child class. • constructors are not inherited in derived class, however the derived class can call default constructor implicitly and if there's a parameterised constructors in bass class then derived class can call it using 'base' keyword.  ____________________________________________  *➤ Rules of Inheritance*  1) C# supports single inheritance, meaning a class can inherit from only one base class. 2) A parent class constructor must be accessible in child class otherwise  inheritance will be not possible. 3) every class, whether user-defined or predefined implicitly derives fr...
Post a Comment
Read more

unit -1 Introduction of Image processing

  What is Image Processing? Image processing is a method to perform operations on an image to enhance it or extract useful information. It is a type of signal processing where the input is an image, and the output may be either an image or characteristics/features associated with that image. Goals of Image Processing Image Enhancement : Improving visual appearance (e.g., contrast, sharpness) Image Restoration : Removing noise or distortion Image Compression : Reducing the amount of data required to represent an image Feature Extraction : Identifying objects, edges, or patterns Image Analysis : Understanding and interpreting image content Object Recognition : Detecting and identifying objects in an image What is an Image? An image is a two-dimensional function f(x, y) , where x and y are spatial coordinates, and f is the intensity (brightness or color) at that point. For digital images, both x, y, and f are finite and discrete. Types of Image Representation...
Post a Comment
Read more