Learning OpenCV

Histogram Equalization

Cameras and image sensors must usually deal not only with the contrast in a scene but also with the image sensors' exposure to the resulting light in that scene. In a standard camera, the shutter and lens aperture settings juggle between exposing the sensors to too much or too little light. Often the range of contrasts is too much for the sensors to deal with; hence there is a trade-off between capturing the dark areas (e.g., shadows), which requires a longer exposure time, and the bright areas, which require shorter exposure to avoid saturating "whiteouts."

Figure 6-20. Two custom distance transform masks

Figure 6-21. First a Canny edge detector was run with param1=100 and param2=200; then the distance transform was run with the output scaled by a factor of 5 to increase visibility

After the picture has been taken, there's nothing we can do about what the sensor recorded; however, we can still take what's there and try to expand the dynamic range of the image. The most commonly used technique for this is histogram equalization. ^[88][89] In Figure 6-22 we can see that the image on the left is poor because there's not much variation of the range of values. This is evident from the histogram of its intensity values on the right. Because we are dealing with an 8-bit ...