Now we can move to basic image processing task. Why do we need to process images? There are several reasons for this. First is, image improvement for human perception to make the image better looking, to improve the subjective image quality. The second goal is, to simplify the subsequent image analysis and recognition. So, it is image improvement from machine perception. Third goal is, image transformation for technical purposes. For example, when we want to display video on a mobile device, we need usually to change image resolution and aspect ratio without damaging the content of the video. And the last reason is, pure entertainment. It's visual effects to get artistic impressions from the effect itself. There are no particular reasons for these effects, but they can be fun. And a lot of cool applications for mobile devices exist that apply visual effects to images. Where are the sources of errors and image defect? Let's look at the image transfer pipeline from real world objects to its presentations on the computer display. First, optical image is formed in the camera. Then, it is passed through the wire pattern to the sensory, where it is discretized. Then, it is transferred via computer network and displayed on the display. In all stages in which data is transformed and during each transformation, there can be errors and edit artifacts which we should process. I will give several examples of image defects that can be compensated by image processing. First, images can be of low contrast with detail difficult to see in bright or dark areas. Second, color tone can look wrong. This picture should be more brownish. In this slide, it is more in blue tones, which is unrealistic. Images can be noisy or blurry because of sharp details. Lighting in captured documents can be non-uniform, where part of the text is very bright and part of the text is very dark. So it's very difficult to recognize such type of document. Image processing can also be used to extract meaningful information from images. One example, I will discuss later, is edge detection. Edges contain a lot of interesting information regarding images and can be used as image features for recognition. A lot of algorithm rely on edge detection.