# One-Step Enhancer: Deblurring and Denoising of OCT Images
## 1 Introduction  
Optical coherence tomography (OCT) is an imaging technology able to produce high-resolution images of living tissues. Most OCT devices used in clinical studies have a resolution of approximately 10 \(\upmu\)m and a depth of penetration up to 2 mm in soft tissues [1]. However, OCT image quality is significantly degraded by speckle noise introduced by the low-coherence interferometry used in the imaging process and by blur arising from relative motions between the device and the tissue [2]. This has a strong impact on subsequent analysis and makes clinical application challenging. Therefore, efficient OCT image enhancement methods are urgently needed [3]. By improving the light source, hardware-based approaches reduce the noise of the detector and scanner to some extent, but the speckle in the imaging system cannot be eliminated. Software-based approaches such as non-local means or block-matching and 3D filtering (BM3D) can provide good results, but need laborious efforts of parameter tuning for different noise levels [4]. Block matching and 4D collaborative filtering (BM4D) expands BM3D to three-dimensional picture volumes [5]. Sliding window, adaptive statistical-based filters, and patch correlation-based filters are the three main classes of digital filters used todenoise images [6]. However, these methods have limitations that reduce their potential for clinical applications, such as a long processing time and excessive smoothness [7]. Recently, convolutional neural networks (CNNs) have started to be considered as a potential solution for such image enhancement tasks. For example, [PERSON] et al. proposed a feed-forward denoising convolutional neural network (DnCNN) able to handle Gaussian denoising with unknown noise levels based on a residual learning strategy [8]. In addition, [PERSON] et al. proposed a self-fusion network that was pre-trained to fuse 3 frames to achieve near-real-time processing frame rates [9]. However, supervised learning methods such as these are laborious in terms of training data acquisition, requiring well-paired training images (images with noise and blur and corresponding clean images). Furthermore, the use of standard CNNs may lead to loss of details due to averaging processes [10]. These characteristics make standard CNNs impractical for OCT image enhancement. To overcome these limitations, [PERSON] et al. proposed a generative adversarial network (GAN) for the problem of restoring low-resolution OCT fundus images to their high-resolution counterparts [11]. In addition, several other methods based on GAN have been proposed for unpaired image enhancement, such as CycleGAN [12], SNR-GAN [10], and SiameseGAN [13]. Another interesting unsupervised learning strategy for OCT image enhancement is disentangled representation. This strategy divides each feature into narrowly specified variables and encodes them as distinct dimensions. Recently, it has been used for image-to-image translation, such as in BicycleGAN [14] and cross-cycleGAN [15]. In addition, DRGAN implemented this unsupervised learning method for reducing speckle with disentangled representation [16]. However, even though these GAN-based models provided promising results for OCT image despeckling, the problem of blurriness of OCT images still needs to be solved. This paper presents a novel solution for simultaneous denoising and deblurring of OCT images without requiring a well-paired training dataset. This is achieved with a deep learning GAN architecture that exploits disentangled representation, as shown in Figure 1. After training, the encoder for content and the generator for a clean image can enhance the original image quality. More specifically, the proposed method learns to disentangle noise, blur and content from raw OCT images and then uses this knowledge to generate enhanced images. In order to accommodate for little content information, Kullback--Leibler (KL) divergence [17] loss is used to regularize the distribution range of extracted blur attributes.