One of the biggest problems in neural learning networks is the lack of training data available to train the network. Data augmentation techniques over the past few years, have therefore been developed, aiming to increase the amount of artificial training data with the limited number of real world samples. In this paper, we look particularly at the MNIST handwritten dataset an image dataset used for digit recognition, and the methods of data augmentation done on this data set. We then take a detailed look into one of the most popular augmentation techniques used for this data set elastic deformation; and highlight its demerit of degradation in the quality of data, which introduces irrelevant data to the training set. To decrease this irrelevancy, we propose to use a similarity measure called Complex Wavelet Structural Similarity Index Measure (CWSSIM) to selectively filter out the irrelevant data before we augment the data set. We compare our observations with the existing augmentation technique and find our proposed method works yields better results than the existing technique.
Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；University of Science and Technology of China（中国科学技术大学）；OPPO Research Institute（OPPO研究院）；The Hong Kong Polytechnic University（香港理工大学）；ShanghaiTech University（上海科技大学）
A New Manifold Distance Measure for Visual Object Categorization
Institute of Applied Mathematics, AMSS, Chinese Academy of Sciences（中国科学院数学与系统科学研究院应用数学研究所）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain
Shanghai Jiao Tong University（上海交通大学）；Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)（中国科学院智能信息处理重点实验室）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）；Pengcheng Laboratory（鹏城实验室）；Alibaba Group（阿里巴巴集团）；Tsinghua University（清华大学）
A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment
Universität Bremen（不来梅大学）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；Berlin Institute of Technology（柏林工业大学）
TRLG: Fragile blind quad watermarking for image tamper detection and recovery by providing compact digests with quality optimized using LWT and GA
Ferdowsi University of Mashhad（马什哈德费尔多西大学）；Computer Engineering Department（计算机工程系）；Computer Engineering Department, Ferdowsi University of Mashhad（马什哈德费尔多西大学计算机工程系）
Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images
University Hospital Magdeburg（马格德堡大学医院）；Otto von Guericke University（奥托·冯·格里克大学）；Human Technopole（人类技术中心）；University of L’Aquila（拉奎拉大学）；Center for Behavioral Brain Sciences（行为脑科学中心）；German Center for Neurodegenerative Disease（德国神经退行性疾病中心）
Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection
School of Electrical Engineering and Computer Science, National University of Sciences and Technology（国家科技大学 电气工程与计算机科学学院）；Information Technology University (ITU)-Punjab（旁遮普信息技术大学）；University of the West of England (UWE)（西英格兰大学）
Wavelet-Fusion Diffusion Model for Multimodal Brain MRI Synthesis with Modality and Metadata Conditioning
Department of Psychology & Neuroscience, Auckland University of Technology（心理学与神经科学系，奥克兰技术大学）；Department of Psychiatry, University of Melbourne（精神病学系，墨尔本大学）
Contrast-Informed Augmentation and Domain-Adversarial Training for Adult-to-Neonatal MR Reconstruction Generalization
University of Calgary（卡尔加里大学）；Seaman Family MR Research Centre, Foothills Medical Centre（Seaman家族磁共振研究中心，山麓医疗中心）；Hotchkiss Brain Institute, University of Calgary（Hotchkiss脑研究所，卡尔加里大学）；Pediatrics, Division of Neonatology, University of Calgary（卡尔加里大学儿科学系新生儿科）；Alberta Children’s Hospital Research Institute, University of Calgary（阿尔伯塔儿童医院研究所，卡尔加里大学）；Radiology and Clinical Neuroscience, University of Calgary（卡尔加里大学放射学与临床神经科学系）；Electrical and Software Engineering, University of Calgary（卡尔加里大学电气与软件工程系）
Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform
Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Walton Institute for Information and Communication Systems Science（沃尔顿信息与通信系统科学研究所）；Waterford Institute of Technology（沃特福德理工学院）；University of Johannesburg（约翰内斯堡大学）；University of Sindh（信德大学）；Sungkyunkwan University（成均馆大学）
pyMEAL: A Multi-Encoder Augmentation-Aware-Learning Toolbox for Robust Medical Image Translation
Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE)（香港脑心血管健康工程中心）；Department of Informatics, Sokoine University of Agriculture（农业科学学院信息系）；Department of Diagnostic Radiology, The University of Hong Kong（香港大学诊断放射科）；State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong（香港大学脑科学与认知科学国家重点实验室）；Alzheimer’s Disease Research Network, The University of Hong Kong（香港大学阿尔茨海默病研究网络）；Department of Biomedical Engineering, City University of Hong Kong（城市大学生物医学工程系）；Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院放射学与放射科学系）；City University of Hong Kong Shenzhen Research Institute（城市大学深圳研究 institute）
Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase Augmentation
1 Institute for Diagnostic；Interventional Radiology, University Medical Center G\"ottingen, Germany 2 Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria 3 Clinic for Neurology, University Medical Center Göttingen, Germany 4 German Centre for Cardiovascular Research (DZHK), partner site Lower Saxony, Germany 5 Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
CTForensics: A Comprehensive Dataset and Method for AI-Generated CT Image Detection
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS）；CAIR, HKISI, Chinese Academy of Sciences（中国科学院计算智能研究中心）；Sangfor Technologies Inc.（深信服技术有限公司）
Evaluation of Machine Learning Reconstruction Techniques for Accelerated Brain MRI Scans
Humber River Hospital（亨伯河医院）；Toronto Western Hospital, University of Toronto（多伦多大学多伦多西区医院）；Foqus Technologies Inc.（Foqus 技术公司）；Robarts Research Institute, Western University（西安大略大学罗巴茨研究所）
MRIComp4Flow: Compression of 3D Brain MRI for Training Multi-Modal Generative Models
Technical University of Munich (TUM)（慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Imperial College London（帝国理工学院）；Florida State University（佛罗里达州立大学）；Institute for Advanced Study, TUM (TUM-IAS)（慕尼黑工业大学高等研究院）；TUM University Hospital（慕尼黑工业大学医院）
