Noise2Noise self-supervised deep-learning holographic despeckling method

Digital holographic microscopy (DHM) is a non-contact and high accuracy measurement technique widely used in biomedicine, microstructure,and other fields.The quality of the reconstructed image and the effectiveness of holographic microscopy were easily affected by speckle noise. Inspired by the idea of Noise2Noise, we propose a self-supervised noise2noise hologram speckle noise removal method. From the holograms that need denoising to generate the input and labels with the same noise distribution to form a training pair for training. Solve the problem that clean holograms are difficult to obtain.The training sets of this self-supervised method are generated from the holograms to be processed. As such, it avoids the need of collectting a large number of training sets. The proposed method is therefore less vulnerable to the background noises and more convenient and reliable for practical hologram speckle denoising applications.