Opt Express. 2022 Feb 14;30(4):5788-5802. doi: 10.1364/OE.447668.
Optical coherence tomography (OCT) is a high-resolution and non-invasive optical imaging technology, which is widely used in many fields. Nevertheless, OCT images are disturbed by speckle noise due to the low-coherent interference properties of light, resulting in significant degradation of OCT image quality. Therefore, a denoising algorithm of OCT images via sparse representation based on noise estimation and global dictionary is proposed in this paper. To remove noise and improve image quality, the algorithm first constructs a global dictionary from high-quality OCT images as training samples and then estimates the noise intensity for each input image. Finally, the OCT images are sparsely decomposed and reconstructed according to the global dictionary and noise intensity. Experimental results indicate that the proposed algorithm efficiently removes speckle noise from OCT images and yield high-quality images. The denoising effect and execution efficiency are evaluated based on quantitative metrics and running time, respectively. Compared with the mainstream adaptive dictionary denoising algorithm in sparse representation and other denoising algorithms, the proposed algorithm exhibits satisfying results in terms of speckle-noise reduction as well as edge preservation, at a reduced computational cost. Moreover, the final denoising effect is significantly better for sets of images with significant variations in noise intensity.
光学相干断层扫描（OCT）是一种高分辨率、非侵入式的光学成像技术，广泛应用于许多领域。然而，由于光的低相干干涉特性，OCT 图像会受到散斑噪声的干扰，导致 OCT 图像质量显著下降。因此，本文提出了一种基于噪声估计和全局字典的稀疏表示的 OCT 图像去噪算法。为了去除噪声并提高图像质量，该算法首先从高质量的 OCT 图像中构建一个全局字典作为训练样本，然后估计每个输入图像的噪声强度。最后，根据全局字典和噪声强度对 OCT 图像进行稀疏分解和重建。实验结果表明，该算法能够有效地去除 OCT 图像中的散斑噪声，并获得高质量的图像。分别基于定量指标和运行时间对去噪效果和执行效率进行了评估。与稀疏表示中的主流自适应字典去噪算法和其他去噪算法相比，该算法在降低计算成本的同时，在减少散斑噪声和保持边缘方面表现出了令人满意的效果。此外，对于噪声强度变化较大的图像集，最终的去噪效果显著更好。
