In deep networks, the lost data details significantly degrade the performances of image segmentation. In this paper, we propose to apply Discrete Wavelet Transform (DWT) to extract the data details during feature map down-sampling, and adopt Inverse DWT (IDWT) with the extracted details during the up-sampling to recover the details. We firstly transform DWT/IDWT as general network layers, which are applicable to 1D/2D/3D data and various wavelets like Haar, Cohen, and Daubechies, etc. Then, we design wavelet integrated deep networks for image segmentation (WaveSNets) based on various architectures, including U-Net, SegNet, and DeepLabv3+. Due to the effectiveness of the DWT/IDWT in processing data details, experimental results on CamVid, Pascal VOC, and Cityscapes show that our WaveSNets achieve better segmentation performances than their vanilla versions.
University of Campinas（坎皮纳斯大学）；Univ Estadual Paulista（圣保罗州立大学）
Image segmentation, the process of separating the elements within an image, is frequently used for obtaining information from photomicrographs. However, segmentation methods should be used with reservations: incorrect segmentation can mislead when interpreting regions of interest (ROI), thus decreasing the success rate of additional procedures. Multi-Level Starlet Segmentation (MLSS) and Multi-Level Starlet Optimal Segmentation (MLSOS) were developed to address the photomicrograph segmentation deficiency on general tools. These methods gave rise to Jansen-MIDAS, an open-source software which a…
University of Bristol（布里斯托大学）；Shanghai Em-Data Technology Co., Ltd.（上海数慧科技股份有限公司）；China University of Mining and Technology（中国矿业大学）；University of Derby（德比大学）
Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper.With a newly defined channelwise consistent feature set as input, the three-d…
Decoupling Wavelet Sub-bands for Single Source Domain Generalization in Fundus Image Segmentation
Indian Statistical Institute（印度统计研究所）；University of Oxford（牛津大学）；Radcliffe Department of Medicine, University of Oxford（牛津大学拉德克利夫医学系）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）
G-images refer to image data defined on irregular graph domains. This work elaborates a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images. To preserve the membership similarity between an arbitrary image pixel and its neighbors, a Kullback-Leibler divergence term on membership partition is introduced as a part of FCM. As a result, similarity-preserving FCM is developed by considering spatial information of image pixels for its robustness enhancement. Due to superior characteristics of a wavelet space, t…
Although spatial information of images usually enhance the robustness of the Fuzzy C-Means (FCM) algorithm, it greatly increases the computational costs for image segmentation. To achieve a sound trade-off between the segmentation performance and the speed of clustering, we come up with a Kullback-Leibler (KL) divergence-based FCM algorithm by incorporating a tight wavelet frame transform and a morphological reconstruction operation. To enhance FCM's robustness, an observed image is first filtered by using the morphological reconstruction. A tight wavelet frame system is employed to decompose …
GeoCueFormer: Geometry-Guided Wavelet Representation and Prediction-Cued Dual-Stage Decoder for Underwater Semantic Segmentation
Southwest University of Science and Technology（西南科技大学）；Columbia University（哥伦比亚大学）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Berkeley（加州大学伯克利分校）
German Aerospace Center (DLR)（德国航空航天中心（DLR））；Technical University of Munich（慕尼黑工业大学）
The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lane-wise traffic management, and urban planning. Lane markings are one of the important components of such maps. Lane markings convey the rules of roads to drivers. While these rules are learned by humans, an autonomous driving vehicle should be taught to learn them to localize itself. Therefore, accurate and reliable lane marking semantic segmentation in the imagery of roads and highways is needed to ach…
Jaypee University of Engineering and Technology（杰伊佩工程技术大学）；Indian Institute of Science Education and Research, Kolkata（印度科学教育与研究所（加尔各答））
In this paper, we carry out a comparative study of the efficacy of wavelets belonging to Daubechies and Coiflet family in achieving image segmentation through a fast statistical algorithm.The fact that wavelets belonging to Daubechies family optimally capture the polynomial trends and those of Coiflet family satisfy mini-max condition, makes this comparison interesting. In the context of the present algorithm, it is found that the performance of Coiflet wavelets is better, as compared to Daubechies wavelet.
WaveFormer: A 3D Transformer with Wavelet-Driven Feature Representation for Efficient Medical Image Segmentation
University of Florida（佛罗里达大学）；University of Central Florida（中佛罗里达大学）；University of California Santa Cruz（加州大学圣克鲁兹分校）；Microsoft Healthcare AI（微软医疗人工智能）；AdventHealth（AdventHealth（ AdventHealth为美国大型非营利医疗保健系统，保留原名称））
Layer-wise Noise Guided Selective Wavelet Reconstruction for Robust Medical Image Segmentation
Big Data and Software College, Chongqing University（重庆大学大数据与软件学院）；Office of Scientific Research, Peking University（北京大学科研办公室）；Key Lab of High Confidence Software Technology, Ministry of Education (Peking University)（教育部高可信软件技术重点实验室（北京大学））；School of Computer Science, Peking University（北京大学计算机学院）；Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital（北京大学第三医院心内科和血管医学研究院）
Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers
RWTH Aachen University（亚琛工业大学）；Iran University of Science and Technology（伊朗科技大学）；Universiti Kebangsaan（马来西亚国民大学）；University of Regensburg（雷根斯堡大学）；Shahid Beheshti University（沙希德·贝赫什提大学）
Instead of directly utilizing an observed image including some outliers, noise or intensity inhomogeneity, the use of its ideal value (e.g. noise-free image) has a favorable impact on clustering. Hence, the accurate estimation of the residual (e.g. unknown noise) between the observed image and its ideal value is an important task. To do so, we propose an $\ell_0$ regularization-based Fuzzy $C$-Means (FCM) algorithm incorporating a morphological reconstruction operation and a tight wavelet frame transform. To achieve a sound trade-off between detail preservation and noise suppression, morpholog…
FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image
Hangzhou Dianzi University（杭州电子科技大学）；College of Media Engineering, Communication University of Zhejiang（浙江传媒学院媒体工程学院）；Shandong University（山东大学）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
针对牙齿 CBCT 中伪影复杂、边界模糊的问题，本文提出 FDNet，用 LF-Wavelet 增强全局语义并结合 SAM 编码器细化边界，在基准上取得最优分割性能。
Technical University of Munich（慕尼黑工业大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Dense pixelwise prediction such as semantic segmentation is an up-to-date challenge for deep convolutional neural networks (CNNs). Many state-of-the-art approaches either tackle the loss of high-resolution information due to pooling in the encoder stage, or use dilated convolutions or high-resolution lanes to maintain detailed feature maps and predictions. Motivated by the structural analogy between multi-resolution wavelet analysis and the pooling/unpooling layers of CNNs, we introduce discrete wavelet transform (DWT) into the CNN encoder-decoder architecture and propose WCNN. The high-freque…
Backdoor Attacks for Remote Sensing Data with Wavelet Transform
Institute of Advanced Research in Artificial Intelligence（人工智能高级研究院）；TU Wien（维也纳工业大学）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；Helmholtz Institute Freiberg for Resource Technology（亥姆霍兹弗赖贝格资源技术研究所）
Image inpainting is the task of filling masked or unknown regions of an image with visually realistic contents, which has been remarkably improved by Deep Neural Networks (DNNs) recently. Essentially, as an inverse problem, the inpainting has the underlying challenges of reconstructing semantically coherent results without texture artifacts. Many previous efforts have been made via exploiting attention mechanisms and prior knowledge, such as edges and semantic segmentation. However, these works are still limited in practice by an avalanche of learnable prior parameters and prohibitive computat…
Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets
Image Processing Center, Beihang University（北京航空航天大学图像处理中心）；Centre pour les Mathématiques et Leurs Applications, Ecole Normale Supérieure of Paris Saclay（巴黎萨克雷高等师范学院数学及其应用中心）；Department of Mathematics and Statistics, San Diego State University（圣地亚哥州立大学数学与统计系）
University of Notre Dame（圣母大学）；Research and Academic Computer Network（研究与学术计算机网络）
This paper offers three new, open-source, deep learning-based iris segmentation methods, and the methodology how to use irregular segmentation masks in a conventional Gabor-wavelet-based iris recognition. To train and validate the methods, we used a wide spectrum of iris images acquired by different teams and different sensors and offered publicly, including data taken from CASIA-Iris-Interval-v4, BioSec, ND-Iris-0405, UBIRIS, Warsaw-BioBase-Post-Mortem-Iris v2.0 (post-mortem iris images), and ND-TWINS-2009-2010 (iris images acquired from identical twins). This varied training data should incr…
Science Foundation Ireland（爱尔兰科学基金会）；CEA-Saclay（法国原子能委员会萨克雷中心）；Université Paris Diderot（巴黎第七大学）
By a "covering" we mean a Gaussian mixture model fit to observed data. Approximations of the Bayes factor can be availed of to judge model fit to the data within a given Gaussian mixture model. Between families of Gaussian mixture models, we propose the Rényi quadratic entropy as an excellent and tractable model comparison framework. We exemplify this using the segmentation of an MRI image volume, based (1) on a direct Gaussian mixture model applied to the marginal distribution function, and (2) Gaussian model fit through k-means applied to the 4D multivalued image volume furnished by the wave…
Multi-scale Vision Transformer (ViT) has emerged as a powerful backbone for computer vision tasks, while the self-attention computation in Transformer scales quadratically w.r.t. the input patch number. Thus, existing solutions commonly employ down-sampling operations (e.g., average pooling) over keys/values to dramatically reduce the computational cost. In this work, we argue that such over-aggressive down-sampling design is not invertible and inevitably causes information dropping especially for high-frequency components in objects (e.g., texture details). Motivated by the wavelet theory, we…
Frequency Domain Enhanced U-Net for Low-Frequency Information-Rich Image Segmentation in Surgical and Deep-Sea Exploration Robots
University of Electronic Science and Technology of China（电子科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；School of Computer Science, Peking University（北京大学计算机科学系）
Graph Convolutional Networks (GCNs) are widely used in a variety of applications, and can be seen as an unstructured version of standard Convolutional Neural Networks (CNNs). As in CNNs, the computational cost of GCNs for large input graphs (such as large point clouds or meshes) can be high and inhibit the use of these networks, especially in environments with low computational resources. To ease these costs, quantization can be applied to GCNs. However, aggressive quantization of the feature maps can lead to a significant degradation in performance. On a different note, Haar wavelet transform…
We propose a highly structured neural network architecture for semantic segmentation with an extremely small model size, suitable for low-power embedded and mobile platforms. Specifically, our architecture combines i) a Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random layer realizing a radial basis function kernel approximation, and iii) a linear classifier. While stages i) and ii) are completely pre-specified, only the linear classifier is learned from data. We apply the proposed architecture to outdoor scene and aerial image semantic segmentation and show that th…
The appearance and structure of blood vessels in retinal images have an important role in diagnosis of diseases. This paper proposes a method for automatic retinal vessel segmentation. In this work, a novel preprocessing based on local histogram equalization is used to enhance the original image then pixels are classified as vessel and non-vessel using a classifier. For this classification, special feature vectors are organized based on responses to Morlet wavelet. Morlet wavelet is a continues transform which has the ability to filter existing noises after preprocessing. Bayesian classifier i…
Utah State University（犹他州立大学）；Harbin Institute of Technology（哈尔滨工业大学）；the First Affiliated Hospital of Harbin Medical University（哈尔滨医科大学附属第一医院）
Breast cancer is one of the most serious disease affecting women's health. Due to low cost, portable, no radiation, and high efficiency, breast ultrasound (BUS) imaging is the most popular approach for diagnosing early breast cancer. However, ultrasound images are low resolution and poor quality. Thus, developing accurate detection system is a challenging task. In this paper, we propose a fully automatic segmentation algorithm consisting of two parts: fuzzy fully convolutional network and accurately fine-tuning post-processing based on breast anatomy constraints. In the first part, the image i…
East China University of Science and Technology（华东理工大学）
Prostate cancer is one of the common diseases in men, and it is the most common malignant tumor in developed countries. Studies have shown that the male prostate incidence rate is as high as 2.5% to 16%, Currently, the inci-dence of prostate cancer in Asia is lower than that in the West, but it is increas-ing rapidly. If prostate cancer can be found as early as possible and treated in time, it will have a high survival rate. Therefore, it is of great significance for the diagnosis and treatment of prostate cancer. In this paper, we propose a trans-fer learning method based on deep neural netwo…
We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described…
University of Campinas（坎皮纳斯大学）；Univ Estadual Paulista（圣保罗州立大学）
Electronic microscopy has been used for morphology evaluation of different materials structures. However, microscopy results may be affected by several factors. Image processing methods can be used to correct and improve the quality of these results. In this paper we propose an algorithm based on starlets to perform the segmentation of scanning electron microscopy images. An application is presented in order to locate gold nanoparticles in natural rubber membranes. In this application, our method showed accuracy greater than 85% for all test images. Results given by this method will be used in…
Med-URWKV†: Toward Enhanced Pretrained Pure VRWKV Models for Medical Image Segmentation
College of Computer Science, Nankai University（南开大学计算机科学学院）；Key Laboratory of Data and Intelligent System Security, Ministry of Education（教育部数据与智能系统安全重点实验室）；School of Medicine, Nankai University（南开大学医学院）；Nankai University Eye institute, Nankai University（南开大学眼科研究院）；Tianjn Eye Hospital（天津眼科医院）；Haihe Lab of ITAI（海河ITAI实验室）
The visual analysis of retina and of its vascular characteristics is important in the diagnosis and monitoring of diseases of visual perception. In the related medical diagnoses, the digital processing of the fundus images is used to obtain the segmentation of retinal vessels. However, an image segmentation is often requiring methods based on peculiar or complex algorithms: in this paper we will show some alternative approaches obtained by applying freely available tools to enhance, without a specific segmentation, the images of the fundus of the eye. We will see in particular, that combining …
Motivation: 3D neuron segmentation is a key step for the neuron digital reconstruction, which is essential for exploring brain circuits and understanding brain functions. However, the fine line-shaped nerve fibers of neuron could spread in a large region, which brings great computational cost to the neuron segmentation. Meanwhile, the strong noises and disconnected nerve fibers bring great challenges to the task. Results: In this paper, we propose a 3D wavelet and deep learning based 3D neuron segmentation method. The neuronal image is first partitioned into neuronal cubes to simplify the segm…
Bridging Modalities and Tasks: A Unified Hierarchical ViT for SAR-to-Optical Translation and Semantic Segmentation
School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；School of Cybersecurity, Northwestern Polytechnical University（西北工业大学网络空间安全学院）
FU-Mamba: A Frequency-Enhanced Dynamic Scanning Framework for Oralscan Image Segmentation
School of Computer Science and Technology, Zhejiang Gongshang University（浙江工商大学计算机科学与技术学院）；Tongxiang Hospital of Traditional Chinese Medicine（桐乡市中医院）；Prince Sattam Bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹王子大学）；Qena University（奎纳大学）；Tampere University（坦佩雷大学）；Universidade Federal Fluminense（弗卢米嫩塞联邦大学）；Federal University of Ceara（塞阿拉联邦大学）；Firat University（菲拉特大学）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；AGH University of Krakow（克拉科夫AGH大学）
Mullard Space Science Laboratory (MSSL)（穆拉德空间科学实验室）；University College London (UCL)（伦敦大学学院）
Segmentation, a useful/powerful technique in pattern recognition, is the process of identifying object outlines within images. There are a number of efficient algorithms for segmentation in Euclidean space that depend on the variational approach and partial differential equation modelling. Wavelets have been used successfully in various problems in image processing, including segmentation, inpainting, noise removal, super-resolution image restoration, and many others. Wavelets on the sphere have been developed to solve such problems for data defined on the sphere, which arise in numerous field…
ACM-UNet: Adaptive Integration of CNNs and Mamba for Efficient Medical Image Segmentation
Wuhan University of Technology（武汉理工大学）；Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院）；Zhongnan Hospital of Wuhan University（武汉大学中南医院）
U-WNO:U-Net-enhanced Wavelet Neural Operator for fetal head segmentation
Thapar Institute of Engineering & Technology（塔帕尔工程技术学院）；Indian Institute of Space Science & Technology（印度空间科学技术学院）；Indian Institute of Public Health（印度公共卫生学院）
FSS-Net: Frequency-Spatial Synergy Network with Wavelet Attention for Carotid Artery Ultrasound Segmentation
Ji luan Academy, Nanchang University（井然学院，南昌大学）；School of Information Engineering, Nanchang University（信息工程学院，南昌大学）；State Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research（水循环与水安全国家重点实验室，中国水利水电科学研究院）；Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong（诊断放射科，李嘉诚医学部，香港大学）；Department of Radiotherapy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University（放疗科，广东省人民医院，广东省医学科学院，南方医科大学）；Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University（SDU-NTU人工智能研究联合中心（C-FAIR），山东大学）；Department of Pediatrics, Shandong Provincial Hospital Affiliated to Shandong First Medical University（儿科，山东省立医院（附属山东第一医科大学））
WaveRNet: Wavelet-Guided Frequency Learning for Multi-Source Domain-Generalized Retinal Vessel Segmentation
School of Software, Xinjiang University, Urumqi, Xinjiang, China（新疆大学软件学院）；The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China（南京中医药大学第一临床医学院）；School of Computer Science, University of Nottingham, UK（诺丁汉大学计算机科学学院）
