In this article, we present a denoising algorithm to improve the interpretation and quality of scanning tunneling microscopy (STM) images. Given the high level of self-similarity of STM images, we propose a denoising algorithm by reformulating the true estimation problem as a sparse regression, often termed sparse coding. We introduce modifications to the algorithm to cope with the existence of artifacts, mainly dropouts, which appear in a structured way as consecutive line segments on the scanning direction. The resulting algorithm treats the artifacts as missing data, and the estimated values outperform those algorithms that substitute the outliers by a local filtering. We provide code implementations for both Matlab and Gwyddion.
NAMF: A Non-local Adaptive Mean Filter for Salt-and-Pepper Noise Removal
China University of Geosciences（中国地质大学）；Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems（湖北省复杂系统先进控制与智能自动化重点实验室）
Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning
Nanjing University（南京大学）；Memorial University of Newfoundland（纽芬兰纪念大学）；Australian National University（澳大利亚国立大学）；Sun Yat-sen University（中山大学）；Wenzhou University（温州大学）
Beam-Shape Effects and Noise Removal from THz Time-Domain Images in Reflection Geometry in the 0.25-6 THz Range
Center for Cultural Heritage Technology, Istituto Italiano di Tecnologia（意大利技术研究院文化遗产技术中心）；Ca’Foscari University of Venice（威尼斯福斯卡里大学）；Stockholm University（斯德哥尔摩大学）
Three-Dimensional Retinal Microvasculature Restoration in OCT Angiography
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学Casey眼科研究所）；Department of Biomedical Engineering, Oregon Health & Science University（俄勒冈健康与科学大学生物医学工程系）
