IEEE/ACM Trans Comput Biol Bioinform. 2024 Jul-Aug;21(4):748-756. doi: 10.1109/TCBB.2022.3205217. Epub 2024 Aug 9.
Although deep learning for Big Data analytics has achieved promising results in the field of optical coherence tomography (OCT) image denoising, the low recognition rate caused by complex noise distribution and a large number of redundant features is still a challenge faced by deep learning-based denoising methods. Moreover, the network with large depth will bring high computational complexity. To this end, we propose a progressive feature fusion attention dense network (PFFADN) for speckle noise removal in OCT images. We arrange densely connected dense blocks in the deep convolution network, and sequentially connect the shallow convolution feature map with the deep one extracted from each dense block to form a residual block. We add attention mechanism to the network to extract the key features and suppress the irrelevant ones. We fuse the output feature maps from all dense blocks and input them to the reconstruction output layer. We compare PFFADN with the state-of-the-art denoising algorithms on retinal OCT images. Experiments show that our method has better improvement in denoising performance.
尽管用于大数据分析的深度学习在光学相干断层扫描 (OCT) 图像去噪领域取得了有希望的结果，但由于复杂的噪声分布和大量冗余特征，深度学习去噪方法仍然面临着低识别率的挑战。此外，深度较大的网络会带来较高的计算复杂度。为此，我们提出了一种渐进式特征融合注意力密集网络 (PFFADN)，用于去除 OCT 图像中的散斑噪声。我们在深度卷积网络中排列密集连接的密集块，并依次将浅层卷积特征图与从每个密集块中提取的深层特征图连接起来，形成残差块。我们在网络中添加注意力机制，以提取关键特征并抑制无关特征。我们融合所有密集块的输出特征图，并将它们输入到重建输出层。我们将 PFFADN 与视网膜 OCT 图像的最先进的去噪算法进行了比较。实验表明，我们的方法在去噪性能方面有了更好的提高。
