Image quality recovery in binary ghost imaging by adding random noise.

Abstract

When the sampling data of ghost imaging are recorded with less bits, i.e., experiencing quantization, a decline in image quality is observed. The fewer bits that are used, the worse the image one gets. Dithering, which adds suitable random noise to the raw data before quantization, is proved to be capable of compensating image quality decline effectively, even for the extreme binary sampling case. A brief explanation and parameter optimization of dithering are given.