Optical Coherence Tomography (OCT) is often employed to observe the retinal layers in the human eyes. The retinal scans are susceptible to artefacts such as head movements or eye blinks. Along with this, the quality of the images is degraded by speckle noise caused due to the constructive and destructive interference of the waves used for capturing data. Recently, image restoration techniques have geared up in terms of quality with the exertion of deep learning. Despeckling using deep learning, in general, necessitates a large set of training images. On the contrary, deep image prior is a novel model that performs denoising operations using a single training image, based on a prior assumption about the noise distribution. This paper extends the concept of the deep image prior towards non-local restoration for speckle noise assuming that the speckle follows Gamma distribution. Such a framework can be incorporated to enhance the OCT images. The proposed framework is assessed qualitatively with visual comparisons and quantitatively using statistical measures like PSNR, CNR and ENL. Comparative studies confirm that the proposed method outperforms the existing methods in restoring speckled input images.

光学相干断层扫描 (OCT) 通常用于观察人眼中的视网膜层。视网膜扫描容易受到人为因素的影响，例如头部运动或眨眼。与此同时，由于用于捕获数据的波的建设性和破坏性干扰而导致的散斑噪声降低了图像的质量。最近，随着深度学习的应用，图像恢复技术在质量方面有所提升。一般来说，使用深度学习去斑需要大量的训练图像。相反，深度图像先验是一种新颖的模型，它基于对噪声分布的先验假设，使用单个训练图像执行去噪操作。假设散斑遵循 Gamma 分布，本文将深度图像先验的概念扩展到散斑噪声的非局部恢复。可以结合这样的框架来增强 OCT 图像。所提出的框架通过视觉比较进行定性评估，并使用 PSNR、CNR 和 ENL 等统计指标进行定量评估。比较研究证实，所提出的方法在恢复斑点输入图像方面优于现有方法。