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Found 38 papers, 12 papers with code
DSFNet: Convolutional Encoder-Decoder Architecture Combined Dual-GCN and Stand-alone Self-attention by Fast Normalized Fusion for Polyps Segmentation
In the past few decades, deep learning technology has been widely used in medical image segmentation and has made significant breakthroughs in the fields of liver and liver tumor segmentation, brain and brain tumor segmentation, video disc segmentation, heart image segmentation, and so on.
BearingPGA-Net: A Lightweight and Deployable Bearing Fault Diagnosis Network via Decoupled Knowledge Distillation and FPGA Acceleration
To the best of our knowledge, this is the first instance of deploying a CNN-based bearing fault diagnosis model on an FPGA.
Deep ReLU Networks Have Surprisingly Simple Polytopes
A ReLU network is a piecewise linear function over polytopes.
Cloud-RAIN: Point Cloud Analysis with Reflectional Invariance
We prove a theorem to explain why Cloud-RAIN can enjoy reflection symmetry.
Rethink Depth Separation with Intra-layer Links
Then, we modify the depth separation theory by showing that a shallow network with intra-layer links does not need to go as wide as before to express some hard functions constructed by a deep network.
Recognizable Information Bottleneck
The recent PAC-Bayes IB uses information complexity instead of information compression to establish a connection with the mutual information generalization bound.
Fast MRI Reconstruction via Edge Attention
To solve this problem, we propose a lightweight and accurate Edge Attention MRI Reconstruction Network (EAMRI) to reconstruct images with edge guidance.
EWT: Efficient Wavelet-Transformer for Single Image Denoising
Transformer-based image denoising methods have achieved encouraging results in the past year.
Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees
In this paper, we propose a randomly projected convex clustering model for clustering a collection of $n$ high dimensional data points in $\mathbb{R}^d$ with $K$ hidden clusters.
PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-Resolution
Stereo image super-resolution aims to boost the performance of image super-resolution by exploiting the supplementary information provided by binocular systems.
One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic Networks
In this paper, with theoretical and empirical studies, we show that quadratic networks enjoy parametric efficiency, thereby confirming that the superior performance of quadratic networks is due to the intrinsic expressive capability.
Multi-Prototypes Convex Merging Based K-Means Clustering Algorithm
Specifically, CM can obtain the optimal merging and estimate the correct k. By integrating these two techniques with K-Means algorithm, the proposed MCKM is an efficient and explainable clustering algorithm for escaping the undesirable local minima of K-Means problem without given k first.
Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI
HP-ALF can perceive the image information in the hierarchical mechanism: image-level perception and patch-level perception.
Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks
In the human brain, neuronal diversity is an enabling factor for all kinds of biological intelligent behaviors.
ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos
The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities.
Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework
In this paper, using a sequential Retinex decomposition strategy, we design a plug-and-play framework based on the Retinex theory for simultaneously image enhancement and noise removal.
Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior
To address this challenge, we suggest a dataset-free deep residual prior for the kernel induced error (termed as residual) expressed by a customized untrained deep neural network, which allows us to flexibly adapt to different blurs and images in real scenarios.
Spherical Image Inpainting with Frame Transformation and Data-driven Prior Deep Networks
Spherical image processing has been widely applied in many important fields, such as omnidirectional vision for autonomous cars, global climate modelling, and medical imaging.
Snow Mask Guided Adaptive Residual Network for Image Snow Removal
Recently, some methods have been proposed for snow removing, and most methods deal with snow images directly as the optimization object.
A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification
The proposed model learns from both data and physics constraints through the training of a deep neural network, which serves as part of the covariance function in GPR.
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation
Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning.
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer
Single-image super-resolution (SISR) has achieved significant breakthroughs with the development of deep learning.
Convex Augmentation for Total Variation Based Phase Retrieval
Phase retrieval is an important problem with significant physical and industrial applications.
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution
Then, we design an efficient Feature Refinement Module (FRM) to enhance the encoded features.
Pixel Screening Based Intermediate Correction for Blind Deblurring
Blind deblurring has attracted much interest with its wide applications in reality.
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications
This paper provides a comprehensive review of epistemic uncertainty learning techniques in supervised learning over the last five years.
From Beginner to Master: A Survey for Deep Learning-based Single-Image Super-Resolution
In this survey, we give an overview of DL-based SISR methods and group them according to their targets, such as reconstruction efficiency, reconstruction accuracy, and perceptual accuracy.
Transformer for Single Image Super-Resolution
LTB is composed of a series of Efficient Transformers (ET), which occupies a small GPU memory occupation, thanks to the specially designed Efficient Multi-Head Attention (EMHA).
Structure-Preserving Deraining with Residue Channel Prior Guidance
Although these methods can remove part of the rain streaks, it is difficult for them to adapt to real-world scenarios and restore high-quality rain-free images with clear and accurate structures.
Rank-One Prior: Toward Real-Time Scene Recovery
To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwater, and haze.
A deep neural network approach on solving the linear transport model under diffusive scaling
In our framework, the solution is approximated by a neural network that satisfies both the governing equation and other constraints.
Numerical Analysis Numerical Analysis
Edge Adaptive Hybrid Regularization Model For Image Deblurring
In the proposed model, it detects the edges and then spatially adjusts the parameters of Tikhonov and TV regularization terms for each pixel according to the edge information.
Joint Demosaicking and Denoising Benefits from a Two-stage Training Strategy
Image demosaicking and denoising are the first two key steps of the color image production pipeline.
Deep Tensor CCA for Multi-view Learning
We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order.
Large-Scale Semi-Supervised Learning via Graph Structure Learning over High-Dense Points
A novel approach is then proposed to construct the graph of the input data from the learned graph of a small number of vertexes with some preferred properties.
A Two-stage Classification Method for High-dimensional Data and Point Clouds
High-dimensional data classification is a fundamental task in machine learning and imaging science.
Linkage between piecewise constant Mumford-Shah model and ROF model and its virtue in image segmentation
The piecewise constant Mumford-Shah (PCMS) model and the Rudin-Osher-Fatemi (ROF) model are two important variational models in image segmentation and image restoration, respectively.
A Three-stage Approach for Segmenting Degraded Color Images: Smoothing, Lifting and Thresholding (SLaT)
In this paper, we propose a SLaT (Smoothing, Lifting and Thresholding) method with three stages for multiphase segmentation of color images corrupted by different degradations: noise, information loss, and blur.
