A Method for Visualization of Images by Photon-Counting Imaging Only Object Locations under Photon-Starved Conditions

Abstract

Recently, many researchers have been studying the visualization of images and the recognition of objects by estimating photons under photon-starved conditions. Conventional photon-counting imaging techniques estimate photons by way of a statistical method using Poisson distribution in all image areas. However, Poisson distribution is temporally and spatially independent, and the reconstructed image has a random noise in the background. Random noise in the background may degrade the quality of the image and make it difficult to accurately recognize objects. Therefore, in this paper, we apply photon-counting imaging technology only to the area where the object is located to eliminate the noise in the background. As a result, it can be seen that the image quality using the proposed method is better than that of the conventional method and the object recognition rate is also higher. Optical experiments were conducted to prove the denoising performance of the proposed method. In addition, we used the structure similarity index measure (SSIM) as a performance metric. To check the recognition rate of the object, we applied the YOLOv5 model. Finally, the proposed method is expected to accelerate the development of astrophotography and medical imaging technologies.