What is a picture gradient?

The image is a term used to describe a continuous shift from light to dark or dark to light. This shift concerns the intensity of one color specifically, which means that one color passes from white, maximum intensity, to black, minimal intensity. During this shift, the color moves, although any permutation of its specific shade. A similar term "colored gradient" concerns the shift from one color to another regardless of intensity. Although these two terms have only superficial similarities, there is a lot of confusion between them and often exchanged unintentionally.

In your heart, the image gradient is as mathematical as graphics. Originally, mathematics was used to create the foundations for gradients before people were able to produce them. These gradients are used as a method of determining the direction and vector speed in the color shift objects. The image -based gradient grew from the applied Physics use and became part of the applied graphics.

When using in graphics, the image gradient is a description of the intensity shift in one color. Intensity is another graphic term that describes the amount of light reflected in color. High intensity means that more color is reflected towards the viewer, leading to color closer to white. Low intensity means that color absorbs more light, resulting in color closer to black. In both cases the basic color is the same; Just absorbs or reflects more colors.

In fact, it creates a gradient of an image that looks like a shift from white to black over one color. When applying to a standard image, individual surfaces are covered with small gradients. In many cases, one picture will have a mixed color area to create a spiritual picture. If the colors were not mixed, it would be clear that the picture will result in a block similar to appearance.

, for example. The computer can look at the individual image gradient and compare it to other covering the image. Then he can find places where different areas of the gradient touch. This information can then be used to find the edges of the subject in the figure and derive the digital representation. This is often used as a way of remedying or improving images to bring further details.