Removing blurring and noise, increasing contrast, and revealing details are examples of enhancement operations. Reducing the noise and blurring and increasing the contrast range could enhance the image. The original image might have areas of very low and high intensity, which mask details. An adaptive enhancement algorithm reveals these details. Adaptive algorithms adjust their operation based on the image information (pixels) being processed. In this case the mean intensity, contrast, and sharpness could be changed based on the pixel-intensity statistics in various areas of the image.
Another enhancement technique assigns colors to pixel intensities, and thus makes small intensity differences more obvious to the human eye. Color could be used, for example, to highlight details in an X-ray image. Image-enhancement operations are often used in image-processing algorithms, and are used in some digital television sets to improve the visual quality of the received picture.
Image restoration improves image quality by using information beyond that in the digital image. This information might be how the image of the scene was formed and what degradations (noise, defocusing, geometric distortions, and so on) occurred in forming or transmitting the image. Movement might blur an image, for example. Sophisticated image-restoration and enhancement algorithms to try to determine the details of the crime, for instance, processed photographs of John F. Kennedy's assassination. Images from spacecraft and satellites are restored and enhanced to reduce the effects of motion, optics, angle of view, noise, and other distortions.
IMAGE ANALYSIS AND RECOGNITION.
Image analysis extracts quantitative information from an image. A high-contrast image of some electronic parts might be made, for example, with each part labeled with a unique color so that the position of each part is found by examining pixels of one color.