PointNorm: Dual Normalization is All You Need for Point Cloud Analysis

Point cloud analysis is challenging due to the irregularity of the point cloud data structure. Existing works typically employ the ad-hoc sampling-grouping operation of PointNet++, followed by sophisticated local and/or global feature extractors for leveraging the 3D geometry of the point cloud. Unfortunately, the sampling-grouping operations do not address the point cloud's irregularity, whereas the intricate local and/or global feature extractors led to poor computational efficiency. In this paper, we introduce a novel DualNorm module after the sampling-grouping operation to effectively and efficiently address the irregularity issue. The DualNorm module consists of Point Normalization, which normalizes the grouped points to the sampled points, and Reverse Point Normalization, which normalizes the sampled points to the grouped points. The proposed framework, PointNorm, utilizes local mean and global standard deviation to benefit from both local and global features while maintaining a faithful inference speed. Experiments show that we achieved excellent accuracy and efficiency on ModelNet40 classification, ScanObjectNN classification, ShapeNetPart Part Segmentation, and S3DIS Semantic Segmentation. Code is available at https://github.com/ShenZheng2000/PointNorm-for-Point-Cloud-Analysis

Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond

This paper presents a comprehensive survey of low-light image and video enhancement. We begin with the challenging mixed over-/under-exposed images, which are under-performed by existing methods. To this end, we propose two variants of the SICE dataset named SICE_Grad and SICE_Mix. Next, we introduce Night Wenzhou, a large-scale, high-resolution video dataset, to address the issue of the lack of a low-light video dataset that discount the use of low-light image enhancement (LLIE) to videos. Our Night Wenzhou dataset is challenging since it consists of fast-moving aerial scenes and streetscapes with varying illuminations and degradation. We conduct extensive key technique analysis and experimental comparisons for representative LLIE approaches using these newly proposed datasets and the current benchmark datasets. Finally, we address unresolved issues and propose future research topics for the LLIE community. Our datasets are available at https://github.com/ShenZheng2000/LLIE_Survey.Comment: 13 pages, 8 tables, and 13 figure

Enhancing Medical Image Segmentation: Optimizing Cross-Entropy Weights and Post-Processing with Autoencoders

The task of medical image segmentation presents unique challenges, necessitating both localized and holistic semantic understanding to accurately delineate areas of interest, such as critical tissues or aberrant features. This complexity is heightened in medical image segmentation due to the high degree of inter-class similarities, intra-class variations, and possible image obfuscation. The segmentation task further diversifies when considering the study of histopathology slides for autoimmune diseases like dermatomyositis. The analysis of cell inflammation and interaction in these cases has been less studied due to constraints in data acquisition pipelines. Despite the progressive strides in medical science, we lack a comprehensive collection of autoimmune diseases. As autoimmune diseases globally escalate in prevalence and exhibit associations with COVID-19, their study becomes increasingly essential. While there is existing research that integrates artificial intelligence in the analysis of various autoimmune diseases, the exploration of dermatomyositis remains relatively underrepresented. In this paper, we present a deep-learning approach tailored for Medical image segmentation. Our proposed method outperforms the current state-of-the-art techniques by an average of 12.26% for U-Net and 12.04% for U-Net++ across the ResNet family of encoders on the dermatomyositis dataset. Furthermore, we probe the importance of optimizing loss function weights and benchmark our methodology on three challenging medical image segmentation tasksComment: Accepted at ICCV CVAMD 202

Pulsed laser linewidth measurement using Fabry–Pérot scanning interferometer

We apply the Fabry–Pérot (FP) scanning interferometer, which is normally used for continuous wave (CW) laser linewidth measurement, for the measurement of pulsed laser linewidths. We analyze the response of the FP interferometer to continuous and pulsed lasers, also different detectors and suitable oscilloscope test parameters being selected for the measurement. For low-speed detectors, we set our oscilloscope to 1-MΩ impedance matching in the sampling mode. For high-speed detectors, we use the same oscilloscope test parameters or 50-Ω impedance matching with the peak-detection mode. With our setup, we achieve on-line linewidth measurement of a nanosecond pulsed laser for single-longitudinal and multi-longitudinal modes. Meanwhile, the linewidth measurement at different pulse repetition rates as low as 1 Hz is also demonstrated. The possibility of detecting the linewidth for pulse widths larger than 100 ps in the 1-μm band is discussed. The application range of the FP scanning interferometer is thus extended to the measurement of pulsed laser linewidths. Keywords: Pulsed laser, Linewidth, Fabry–Pérot interferomete