University of Isfahan, Department of Artificial Intelligence, Faculty of Computer Engineering, Isfah, Iran.
Hunan University, College of Electrical and Information Engineering, Changsha, China.
J Biomed Opt. 2018 Mar;23(3):1-11. doi: 10.1117/1.JBO.23.3.036011.
We present a nonlocal weighted sparse representation (NWSR) method for reconstruction of retinal optical coherence tomography (OCT) images. To reconstruct a high signal-to-noise ratio and high-resolution OCT images, utilization of efficient denoising and interpolation algorithms are necessary, especially when the original data were subsampled during acquisition. However, the OCT images suffer from the presence of a high level of noise, which makes the estimation of sparse representations a difficult task. Thus, the proposed NWSR method merges sparse representations of multiple similar noisy and denoised patches to better estimate a sparse representation for each patch. First, the sparse representation of each patch is independently computed over an overcomplete dictionary, and then a nonlocal weighted sparse coefficient is computed by averaging representations of similar patches. Since the sparsity can reveal relevant information from noisy patches, combining noisy and denoised patches' representations is beneficial to obtain a more robust estimate of the unknown sparse representation. The denoised patches are obtained by applying an off-the-shelf image denoising method and our method provides an efficient way to exploit information from noisy and denoised patches' representations. The experimental results on denoising and interpolation of spectral domain OCT images demonstrated the effectiveness of the proposed NWSR method over existing state-of-the-art methods.
我们提出了一种用于重建视网膜光学相干断层扫描（OCT）图像的非局部加权稀疏表示（NWSR）方法。为了重建高信噪比和高分辨率的 OCT 图像，需要利用高效的去噪和插值算法，特别是在采集过程中原数据被欠采样的情况下。然而，OCT 图像受到高水平噪声的影响，这使得稀疏表示的估计成为一项困难的任务。因此，所提出的 NWSR 方法将多个相似的噪声和去噪斑块的稀疏表示合并，以更好地估计每个斑块的稀疏表示。首先，在过完备字典上独立计算每个斑块的稀疏表示，然后通过平均相似斑块的表示来计算非局部加权稀疏系数。由于稀疏性可以从噪声斑块中揭示相关信息，因此结合噪声和去噪斑块的表示对于获得未知稀疏表示的更稳健估计是有益的。去噪斑块是通过应用现成的图像去噪方法获得的，我们的方法提供了一种从噪声和去噪斑块表示中利用信息的有效方法。在光谱域 OCT 图像的去噪和插值实验结果表明，所提出的 NWSR 方法优于现有的最先进方法。
