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Convolutional kernels are basic and vital components of deep Convolutional Neural Networks (CNN). In this paper, we equip convolutional kernels with shape attributes to generate the deep Irregular Convolutional Neural Networks (ICNN).…

Computer Vision and Pattern Recognition · Computer Science 2017-06-27 Jiabin Ma , Wei Wang , Liang Wang

We introduce a novel weighted convolution operator that enhances traditional convolutional neural networks (CNNs) by integrating a spatial density function into the convolution operator. This extension enables the network to differentially…

Computer Vision and Pattern Recognition · Computer Science 2025-06-02 Simone Cammarasana , Giuseppe Patanè

Convolutional networks are not aware of an object's geometric variations, which leads to inefficient utilization of model and data capacity. To overcome this issue, recent works on deformation modeling seek to spatially reconfigure the data…

Computer Vision and Pattern Recognition · Computer Science 2020-02-13 Hang Gao , Xizhou Zhu , Steve Lin , Jifeng Dai

The single image super-resolution task is one of the most examined inverse problems in the past decade. In the recent years, Deep Neural Networks (DNNs) have shown superior performance over alternative methods when the acquisition process…

Computer Vision and Pattern Recognition · Computer Science 2020-05-27 Shady Abu Hussein , Tom Tirer , Raja Giryes

Deformable image registration is a fundamental task in medical imaging. Due to the large computational complexity of deformable registration of volumetric images, conventional iterative methods usually face the tradeoff between the…

Computer Vision and Pattern Recognition · Computer Science 2022-01-19 Kaicong Sun , Sven Simon

In "extreme" computational imaging that collects extremely undersampled or noisy measurements, obtaining an accurate image within a reasonable computing time is challenging. Incorporating image mapping convolutional neural networks (CNN)…

Machine Learning · Statistics 2023-08-31 Il Yong Chun , Jeffrey A. Fessler

As deep neural networks are increasingly used in applications suited for low-power devices, a fundamental dilemma becomes apparent: the trend is to grow models to absorb increasing data that gives rise to memory intensive; however low-power…

Computer Vision and Pattern Recognition · Computer Science 2023-02-21 Weiyu Guo , Jiabin Ma , Liang Wang , Yongzhen Huang

Neural implicit functions have achieved impressive results for reconstructing 3D shapes from single images. However, the image features for describing 3D point samplings of implicit functions are less effective when significant variations…

Computer Vision and Pattern Recognition · Computer Science 2022-02-01 Yixin Zhuang , Yunzhe Liu , Yujie Wang , Baoquan Chen

Medical image segmentation plays a vital role in various clinical applications, enabling accurate delineation and analysis of anatomical structures or pathological regions. Traditional CNNs have achieved remarkable success in this field.…

Image and Video Processing · Electrical Eng. & Systems 2024-04-18 Seyed M. R. Modaresi , Aomar Osmani , Mohammadreza Razzazi , Abdelghani Chibani

Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene. In this paper we consider the dense approach instead of the more common sparse paradigm, thus striving to find…

Computer Vision and Pattern Recognition · Computer Science 2022-11-28 Johan Edstedt , Ioannis Athanasiadis , Mårten Wadenbäck , Michael Felsberg

Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to…

Computer Vision and Pattern Recognition · Computer Science 2017-11-27 Jo Schlemper , Jose Caballero , Joseph V. Hajnal , Anthony Price , Daniel Rueckert

Recently, single-image super-resolution has made great progress owing to the development of deep convolutional neural networks (CNNs). The vast majority of CNN-based models use a pre-defined upsampling operator, such as bicubic…

Computer Vision and Pattern Recognition · Computer Science 2019-08-28 Xin Yang , Haiyang Mei , Jiqing Zhang , Ke Xu , Baocai Yin , Qiang Zhang , Xiaopeng Wei

Various Graph Neural Networks (GNNs) have been successful in analyzing data in non-Euclidean spaces, however, they have limitations such as oversmoothing, i.e., information becomes excessively averaged as the number of hidden layers…

Machine Learning · Computer Science 2024-01-23 Jaeyoon Sim , Sooyeon Jeon , InJun Choi , Guorong Wu , Won Hwa Kim

Pre-trained vision foundation models (VFMs) provide strong semantic representations, yet their patch-level features are inherently coarse, limiting their effectiveness on tasks requiring fine-grained localization, dense prediction, and…

Computer Vision and Pattern Recognition · Computer Science 2026-05-21 Wentong Li , Zhiyuan Qi , Zichen Zhao , Kai Zhang , Lei Zhang

Image super-resolution (SR) in resource-constrained scenarios demands lightweight models balancing performance and latency. Convolutional neural networks (CNNs) offer low latency but lack non-local feature capture, while Transformers excel…

Computer Vision and Pattern Recognition · Computer Science 2025-08-19 Quanwei Hu , Yinggan Tang , Xuguang Zhang

Self-similarity learning has been recognized as a promising method for single image super-resolution (SR) to produce high-resolution (HR) image in recent years. The performance of learning based SR reconstruction, however, highly depends on…

Computer Vision and Pattern Recognition · Computer Science 2018-09-28 Jiahe Shi , Chun Qi

Diffusion models have achieved significant progress in image generation. The pre-trained Stable Diffusion (SD) models are helpful for image deblurring by providing clear image priors. However, directly using a blurry image or pre-deblurred…

Computer Vision and Pattern Recognition · Computer Science 2025-02-07 Lingshun Kong , Jiawei Zhang , Dongqing Zou , Jimmy Ren , Xiaohe Wu , Jiangxin Dong , Jinshan Pan

We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals. To this…

Computer Vision and Pattern Recognition · Computer Science 2019-01-09 Chiyu "Max" Jiang , Jingwei Huang , Karthik Kashinath , Prabhat , Philip Marcus , Matthias Niessner

The deep convolutional neural networks (CNNs)-based single image dehazing methods have achieved significant success. The previous methods are devoted to improving the network's performance by increasing the network's depth and width. The…

Computer Vision and Pattern Recognition · Computer Science 2022-09-07 Pinjun Luo , Guoqiang Xiao , Xinbo Gao , Song Wu

With the impressive capability to capture visual content, deep convolutional neural networks (CNN) have demon- strated promising performance in various vision-based ap- plications, such as classification, recognition, and objec- t…

Computer Vision and Pattern Recognition · Computer Science 2015-09-16 Zhen Liu