Describe the model of image degradation/ restoration process. State the various noise models

The image degradation/restoration process is a model used in image processing to improve the quality of an image by reversing the process of degradation. The model can be described in the following steps:

1. Image Acquisition: This is the first step where an image is captured by a sensor (like a camera).

2. Degradation: After the image is acquired, it goes through a degradation process. This process can be due to various factors like noise, blurring, camera misfocus, motion blur, etc. Mathematically, this can be represented as g(x, y) = h(x, y) * f(x, y) + n(x, y) where g(x, y) is the degraded image, h(x, y) is the degradation function, f(x, y) is the original image, and n(x, y) is the noise.

3. Restoration: The aim of the restoration process is to recover the original image from the degraded image. This is done by reversing the degradation process using various algorithms and techniques.

Various noise models used in the degradation process are:

1. Gaussian Noise: This is statistical noise having a probability density function equal to the normal distribution, also known as Gaussian distribution. In images, it is usually caused by sensor noise due to poor illumination and/or high temperature.

2. Salt-and-Pepper Noise: This type of noise presents itself as sparsely occurring white and black pixels. It is caused by sharp, sudden disturbances in the image signal.

3. Poisson Noise: Also known as shot noise. This type of noise can be modeled by a Poisson process. It is inherent to the image acquisition process itself, particularly in lower light conditions.

4. Speckle Noise: This is a granular noise that inherently exists in and degrades the quality of the active radar and synthetic aperture radar (SAR) images. It increases the mean grey level of a local area.

5. Impulse Noise: This is a type of noise in which each pixel has an independent chance of being contaminated by noise. It can be caused by bit errors in transmission, faulty memory locations in hardware, or timing errors in the operation of a scanner or display.

Each of these noise models has a different effect on images, and understanding these effects is crucial for developing effective noise reduction algorithms.

The image degradation/restoration process is a model used in image processing to improve the quality of a degraded image. This process involves two main steps: degradation and restoration.

1. Image Degradation: This is the first step in the process where the original image is degraded using a degradation function and added noise. The degradation function represents the various ways an image can be degraded such as blurring, distortion, etc. The noise represents the random variations of brightness or color information in images, and is usually an aspect of electronic noise.

2. Image Restoration: This is the second step where the degraded image is restored to its original form. This is done by reversing the degradation function and removing the added noise. The goal of this step is to recover the original image as closely as possible.

Various noise models are used in the image degradation/restoration process. These include:

1. Gaussian Noise: This is statistical noise having a probability density function equal to that of the normal distribution, which is also known as the Gaussian distribution.

2. Salt-and-Pepper Noise: This is a form of noise typically seen on images. It presents itself as sparsely occurring white and black pixels.

3. Poisson Noise: This is a type of noise which can be modeled by a Poisson process. It is also known as shot noise.

4. Speckle Noise: This is a granular interference that inherently exists in and degrades the quality of the active radar and synthetic aperture radar (SAR) images.

5. Impulse Noise: This is a type of noise in which each pixel is replaced with a random value with a certain probability.

Each of these noise models has a different effect on images and requires different techniques for noise reduction during the restoration process.