JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech III IMAGE PROCESSING Previous Question Paper (September - 2021 - R18)

Digital Image Processing refers to the manipulation of digital images using a computer. It involves the use of algorithms to perform operations such as enhancement, restoration, compression, and segmentation on an image represented as a matrix of discrete numerical values.

The origins of digital image processing date back to the early 1920s with the Bartlane cable picture transmission service, which used specialized printing equipment to transmit images across the Atlantic via submarine cable. The field advanced significantly in the 1960s with the development of high-speed computers and the Apollo space program, where NASA's Jet Propulsion Laboratory used digital techniques to enhance images of the moon taken by Ranger spacecraft. These early efforts laid the foundation for modern medical imaging, satellite remote sensing, and computer vision.

Gray level digital image processing involves operations performed on images where each pixel represents only intensity information, typically ranging from 0 (black) to 255 (white) in an 8-bit system. Unlike color images, which contain RGB channels, gray level images are represented by a single two-dimensional matrix.

A common example is Image Thresholding, which is used to convert a grayscale image into a binary image. For instance, if an image contains a dark object on a light background, we can apply a threshold value (e.g., 128). Any pixel with a value less than 128 is set to 0 (black), and any pixel with a value greater than or equal to 128 is set to 255 (white). This process simplifies the image data, making it easier to perform object detection or edge extraction.

Another example is Histogram Equalization, which adjusts the contrast of a gray level image by redistributing the intensity values to cover the full range of the histogram, thereby enhancing the visibility of features in images with poor lighting.