Digital image processing using Python language
Devanand Bhonsle, Ravi Shankar Mishra, Swati Chaitandas Hadke, Anupama Mohabansi, Prajakta Vinayak Upadhye, Sheetal Mungale · 2024
In this chapter a methodological study on image processing has been done, which may be used in various applications. The quality of output image must always be enhanced version of input image. The quality of the image degrades due to various reasons, in which introduction of noise signal plays a significant role. During the acquisition of an image some unwanted signals may be introduced in it, which deteriorate the quality of the image. Hence it is required to remove these noise signals. However, it is challenging to eliminate the interference from noise signals entirely. Nevertheless, their impact can be reduced to a degree that is deemed acceptable for various applications. To remove these noise signals it is desired to study the characteristics of different noise signals, which may help the researchers to make a decision about the selection of a filter for a particular noise signal. However, applying the filtering technique may lead to the suppression of important information like edges, ridges, contours and other fine details which may be helpful for various applications of image processing. Therefore, the goal is to decrease the noise signals without sacrificing the fine details of the images. Image denoising is a preprocessing task. This image is now ready for other operations such as image segmentation, edge detection, object recognition, image sharpening, classification, feature detection and matching, analysis and manipulation of image, etc. To perform all the aforementioned operations, MATLAB is a popular toolbox used for image processing. However it can be done using other software and codes can be written using various programming languages. Python is one of the languages which can be used for image processing.