Skeleton extraction and inpainting from poor, broken ESPI fringe with an M-net convolutional neural network.

Extracting skeletons from fringe patterns is the key to the fringe skeleton method, which is used to extract phase terms in electronic speckle pattern interferometry (ESPI). Because of massive inherent speckle noise, extracting skeletons from poor, broken ESPI fringe patterns is challenging. In this paper, we propose a method based on a modified M-net convolutional neural network for skeleton extraction from poor, broken ESPI fringe patterns.

Texture preservation and speckle reduction in poor optical coherence tomography using the convolutional neural network.

For a poor quality optical coherence tomography (OCT) image, quality enhancement is limited to speckle residue and edge blur as well as texture loss, especially at the background region near edges. To solve this problem, in this paper we propose a de-speckling method based on the convolutional neural network (CNN). In the proposed method, we use a deep nonlinear CNN mapping model in the serial architecture, here named as OCTNet.

Batch denoising of ESPI fringe patterns based on convolutional neural network.

The denoising of electronic speckle pattern interferometry (ESPI) fringe patterns is a key step in the application of ESPI. In this paper, we propose a method for batch denoising of ESPI fringe patterns based on a convolution neural network (CNN). In the proposed method, the network is first trained by our training dataset, which consists of the noisy ESPI fringe patterns and the corresponding noise-free images.