# One-Step Enhancer: Deblurring and Denoising of OCT Images
## 5 Conclusions  
This paper presented a novel deep learning model for one-step denoising and deblurring of OCT images. This one-step enhancer (OSE) is trained using an unsupervised learning strategy, which allows learning from a mixed dataset of unpaired OCT images. For this, the method uses disentangled representation and generative adversarial network (GAN) to extract content, blur and noise features from raw OCT images, and then learns to generate clean images. The proposed OSE was assessed through both an ablation study and a comparative performance evaluation based on the quantitative metrics PSNR and SSIM. The ablation study demonstrated that the method produced effective denoise and deblur modules, which enabled high performance levels when used in a combined model. The comparative analysis showed the proposed method outperformed state-of-the-art methods for OCT image enhancement, indicating that our one-step enhancer is a valuable alternative for speckle and blur reduction in OCT images. Conceptualization, S.L. ; Data curation, A.G.; Investigation, S.L. and L.S.M. ; Methodology, S.L. and L.S.M. ; Software, S.L. and M.A.A. ; Supervision, L.S.M. ; Writing--original draft, S.L. ; Writing--review & editing, M.A.A., A.G. and L.S.M. All authors have read and agreed to the published version of the manuscript. This research  
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