Dispersion in optical coherence tomography (OCT) poses a challenge that is exacerbated by the increased spectral bandwidth, which leads to image blur and feature loss.In this paper, we present a straightforward and cost-effective approach for dispersion compensation in OCT.To achieve this, we employed a pixel-to-pixel (Pix2Pix) generative adversarial network (GAN) architecture customized for image-to-image translation.Two data groups with varying amounts GRINDERS of training image data and epochs were used.
The Pix2Pix GAN was trained to generate clear OCT images from the corresponding dispersion-affected OCT images in paired datasets.According to the experimental Underseat Storage results, the Pix2Pix GAN technique demonstrated a substantial improvement over the basic GAN.Specifically, it increases the peak signal-to-noise ratio (PSNR) by 159%, structural similarity index (SSIM) by 370%, and Fréchet inception distance (FID) by 274%.These outcomes indicate that the proposed model can generate images with resilience and effectiveness, particularly when dealing with dispersion-affected OCT data.