RepE: unsupervised representation learning for image enhancement in nonlinear optical microscopy

We present an unsupervised learning denoising method, RepE (representation and enhancement), designed for nonlinear optical microscopy images, such as second harmonic generation (SHG) and two-photon fluorescence (TPEF). Addressing the challenge of effectively denoising images with various noise types, RepE employs an encoder network to learn noise-free representations and a reconstruction network to generate denoised images. It offers several key advantages, including its ability to (i) operate without restrictive statistic assumptions, (ii) eliminate the need for clean-noisy pairs, and (iii) requires only a few training images. Comparative evaluations on real-world SHG and TPEF images from esophageal cancer tissue slides (ESCC) demonstrate that our method outperforms existing techniques in image quality metrics. The proposed method provides a practical, robust solution for denoising nonlinear optical microscopy images, and it has the potential to be extended to other nonlinear optical microscopy modalities.

Continuous Finger Gesture Recognition Based on Flex Sensors

The goal of this work is to present a novel continuous finger gesture recognition system based on flex sensors. The system is able to carry out accurate recognition of a sequence of gestures. Wireless smart gloves equipped with flex sensors were implemented for the collection of the training and testing sets. Given the sensory data acquired from the smart gloves, the gated recurrent unit (GRU) algorithm was then adopted for gesture spotting. During the training process for the GRU, the movements associated with different fingers and the transitions between two successive gestures were taken into consideration. On the basis of the gesture spotting results, the maximum a posteriori (MAP) estimation was carried out for the final gesture classification. Because of the effectiveness of the proposed spotting scheme, accurate gesture recognition was achieved even for complicated transitions between successive gestures. From the experimental results, it can be observed that the proposed system is an effective alternative for robust recognition of a sequence of finger gestures.