Digital Image Processing (DIP) involves a sequence of steps used to improve the quality and interpretability of digital images.
The first step is image acquisition, which means capturing images using sensors or scanners and converting them into digital form.
Next is pre-processing, which includes correction of distortions, noise removal, and image enhancement to improve visual quality.
Image segmentation follows, dividing the image into meaningful regions or objects for easier analysis.
Then comes image classification, where pixels are categorized into classes or themes based on spectral signatures.
Post-processing may involve filtering, smoothing, or combining results for better accuracy.
Finally, information extraction and interpretation convert processed data into usable information for decision-making.
These steps ensure that raw images are transformed into valuable, meaningful data.