Image Processing Fundamentals

The fundamental steps include image acquisition, image enhancement, image restoration, image compression, and image segmentation.

The basic elements include pixels, image data, image formats, and processing techniques such as filtering and enhancement.

Pixels are the smallest units of a digital image, and they are arranged in a grid, with each pixel representing a specific color or intensity.

<p>An analog image is converted to digital by sampling the continuous data and quantizing the sampled values to a finite set of levels.</p> Signup and view all the answers

<p>The human eye perceives images through the retina, which converts light into electrical signals sent to the brain, where they are interpreted as visual information.</p> Signup and view all the answers

<p>Methods include capturing images using cameras, scanners, or sensors, each employing different techniques like CCD or CMOS.</p> Signup and view all the answers

<p>Image transformations, such as Fourier transforms, are vital for frequency analysis, filtering, and data compression, enhancing the interpretability of images.</p> Signup and view all the answers

<p>Separability in a 2D Fourier transform allows for the independent processing of rows and columns, simplifying computational complexity and analysis.</p> Signup and view all the answers

<p>Lossless compression retains all original data without any loss, while lossy compression reduces file size by removing some data, which can affect quality.</p> Signup and view all the answers

<p>The key properties include linearity, shift invariance, and symmetry. It also has applications in image processing for frequency analysis.</p> Signup and view all the answers

<p>Redundancy in images refers to duplicate or unnecessary information that can be eliminated without loss of quality, which is crucial for efficient image compression.</p> Signup and view all the answers

<p>DCT transforms image data into frequency components, allowing for efficient compression by separating the image into varying levels of importance.</p> Signup and view all the answers

<p>The Haar transform is significant for its simplicity and fast computation, particularly in image compression and analysis. It decomposes a signal into average and difference values.</p> Signup and view all the answers

<p>Edge detection identifies boundaries between different regions in an image, which is essential for accurately segmenting and analyzing image content.</p> Signup and view all the answers

<p>The Hadamard transform is used for efficient computation of the transform matrix and helps in image compression and error correction. Its operations are simple and easily implementable.</p> Signup and view all the answers

<p>The Walsh Transform is applied to represent signals with orthogonal functions, aiding in efficient data compression and error correction. It replaces conventional Fourier series in some applications.</p> Signup and view all the answers

<p>Key attributes in image enhancement are contrast, brightness, sharpness, and overall clarity of the image.</p> Signup and view all the answers

<p>Run-length Coding is a compression technique that replaces sequences of the same data value occurring in consecutive data points with a single data value and a count, effectively reducing file size.</p> Signup and view all the answers

<p>The Fast Fourier Transform (FFT) is an algorithm to compute the discrete Fourier transform efficiently. Its main advantages are reduced computational complexity and speed in processing signals.</p> Signup and view all the answers

<p>Shannon-Fano coding is a method for creating variable-length codes for encoding symbols based on their frequencies, allowing more common symbols to have shorter codes, optimizing storage.</p> Signup and view all the answers

<p>Histogram equalization improves the contrast of images by redistributing pixel intensity values to cover the full range. This results in a more uniform histogram.</p> Signup and view all the answers

<p>Image restoration is the process of recovering an image that has been degraded by various factors such as noise or blur. It uses model-based approaches to reverse these degradations.</p> Signup and view all the answers

<p>Image restoration focuses on correcting or recovering degraded images by reversing the degradation process, while image enhancement aims to improve the visual appearance of images.</p> Signup and view all the answers

<p>Image smoothing techniques reduce noise and details to create a blurred effect, while sharpening techniques enhance edges and fine details to increase clarity. Each serves different purposes in image processing.</p> Signup and view all the answers

<p>Image segmentation is the process of partitioning an image into multiple segments to simplify or change the representation of the image. Applications include object detection, medical imaging, and autonomous driving.</p> Signup and view all the answers

<p>The three types of discontinuity are point discontinuity, line discontinuity, and surface discontinuity.</p> Signup and view all the answers

<p>The Gradient Operator is a method used to calculate the change in intensity or color in an image, highlighting edges. It is often represented by operators like Sobel, Prewitt, or Roberts.</p> Signup and view all the answers

<p>Run-Length Encoding is a simple form of data compression that replaces sequences of the same data value with a single value and count. For example, 'AAAABBBCCDAA' would be encoded as '4A3B2C1D2A'.</p> Signup and view all the answers

<p>Redundancy in images refers to unnecessary data that can be removed without losing information, such as repeated patterns or colors. Reducing redundancy is essential for efficient image compression.</p> Signup and view all the answers

<p>Image compression reduces the file size without significantly degrading quality, which helps in storage efficiency and faster transmission. It is essential for web images, video streaming, and digital photography.</p> Signup and view all the answers

<p>Lossy compression reduces file size by removing some data permanently, leading to a loss in quality, whereas error-free (lossless) compression allows the original image to be perfectly reconstructed from the compressed data.</p> Signup and view all the answers

<p>Shannon-Fano coding is a method for lossless data compression where symbols are assigned codes based on their frequency of occurrence. For example, given symbols A (5), B (3), and C (2), A might get '0', B '10', and C '11'.</p> Signup and view all the answers