Abstract
In recent years, convolutional neural networks (CNN) have been widely used in image denoising for their high performance. One difficulty in applying the CNN to medical image denoising such as speckle reduction in the optical coherence tomography (OCT) image is that a large amount of high-quality data is required for training, which is an inherent limitation for OCT despeckling. Recently, deep image prior (DIP) networks have been proposed for image restoration without pre-training since the CNN structures have the intrinsic ability to capture the low-level statistics of a single image. However, the DIP has difficulty finding a good balance between maintaining details and suppressing speckle noise. Inspired by DIP, in this paper, a sorted non-local statics which measures the signal autocorrelation in the differences between the constructed image and the input image is proposed for OCT image restoration. By adding the sorted non-local statics as a regularization loss in the DIP learning, more low-level image statistics are captured by CNN networks in the process of OCT image restoration. The experimental results demonstrate the superior performance of the proposed method over other state-of-the-art despeckling methods, in terms of objective metrics and visual quality.
Highlights
Medical images are an important application of digital imaging combined with biology to visually express important medical information
Spatial filtering techniques are mainly represented by non-local means (NLM) methods [8,9,10,11,12], which have the advantage of the self-similarity of natural images by comparing patches with non-local neighborhoods [8]
By minimizing the signal autocorrelation loss in the deep image prior (DIP) learning, more non-local similarity image statistics are captured by convolutional neural networks (CNN) in the process of Optical coherence tomography (OCT) image restoration
Summary
Medical images are an important application of digital imaging combined with biology to visually express important medical information. In sparsity-based simultaneous denoising and interpolation (SBSDI) [15], the dictionaries are improved by constructing from previously collected datasets high-SNR images from the target imaging subject These sparse representation techniques can preserve most image details, but they tend to leave some noise in the despeckling result. The denoising performance of CNN can be improved by symmetric convolutional-deconvolutional layers [22] and the prior observation model [23] To train their numerous parameters, these networks generally require large amounts of high-quality data, which is an inherent limitation for OCT despeckling. By minimizing the signal autocorrelation loss in the DIP learning, more non-local similarity image statistics are captured by CNN in the process of OCT image restoration. (2) The sorted non-local statics is used as an autocorrelation loss in the deep image prior learning framework to get rid of the speckle noise in the OCT image
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