3.5 | Evaluation of the deep learning network invariance with various noise distributions
Lastly, we evaluated the network’s invariancy with respect to different noise types such as Gaussian, S&P, etc. As observed in Fig. 8, the images in the 2nd row are corrupted with Gaussian white noise (variance = 0.01), and the 4th row displays S&P noise corrupted images of subcutaneous tumors. The 3rd and 5th rows are the corresponding denoised U-Net outcomes. Our denoising U-Net efficiently removed the Gaussian white noise whereas it is unable to reduce S&P noise types. The reason for the good performance of the U-Net in removing the Gaussian noise is mainly because the noise distribution of the training images captured from the LED Acoustic-X system somewhat looks similar to the Gaussian noise type which essentially holds the i.i.d (independent and identically distributed) assumption. However, the network is not effective at removing the S&P noise which needs further investigation. Generally, the electronic noise comprises of a variety of thermal and shot noise profiles that can be approximated to Gaussian distribution, and thus our denoising algorithm will not have any difficulty in handling such types of noise. Due to this capability, the U-Net algorithm developed here has a potential of being an electronic platform independent denoiser and further studies are under way to evaluate this feature.