22 papers · ranked by Valyu relevance
Marcos D. Medeiros, Luiz Marcos G. Gonçalves, Alejandro C. Frery
Stereo matching is an open problem in Computer Vision, for which local features are extracted to identify corresponding points in pairs of images. The results are heavily dependent on the initial steps. We apply image decomposition in multiresolution levels, for reducing the search space, computational time, and…
Hongyan Zhou, Liang Yao
—The current learning process of deep learning, regardless of any deep neural network (DNN) architecture and/or learning algorithm used, is essentially a single resolution training. We explore multiresolution learning and show that multiresolution learning can significantly improve robustness of DNN models for both 1D…
Lamiaa Abdel-Hamid
Chest computer tomography (CT) provides a readily available and efficient tool for COVID-19 diagnosis. Wavelet and contourlet transforms have the advantages of being localized in both space and time. In addition, multiresolution analysis allows for the separation of relevant image information in the different subbands.…
Shaolong Chen, Changzhen Qiu, Weiping Yang, Zhiyong Zhang
The automatic segmentation of cardiac magnetic resonance (MR) images is the basis for the diagnosis of cardiac-related diseases. However, the segmentation of cardiac MR images is a challenging task due to the inhomogeneity of MR images intensity distribution and the unclear boundaries between adjacent tissues. In this…
Andrei Dabravolski, Kees Joost Batenburg, Jan Sijbers, Kewei Chen
The proposed multiresolution DART algorithm starts a reconstruction on a coarse reconstruction grid and then uses the resampled resulting reconstruction as an initial point for a new reconstruction process on a finer grid, iteratively switching to the new grid until the target pixel size is reached. In our experiments…
Behrooz Nasihatkon, Fredrik Kahl
—We study the relation between the target functions of low-resolution and high-resolution intensity-based registration for the class of rigid transformations. Our results show that low resolution target values can tightly bound the high-resolution target function in natural images. This can help with analyzing and…
Shaolong Chen, Changzhen Qiu, Weiping Yang, Zhiyong Zhang + 1 more
'Alessandro Bevilacqua'] The latest medical image segmentation methods uses UNet and transformer structures with great success. Multiscale feature fusion is one of the important factors affecting the accuracy of medical image segmentation. Existing transformer-based UNet methods do not comprehensively explore…
Zhiqiang Tian, Yudiao Wang, Shaoyi Du, Xuguang Lan + 1 more
Generative adversarial networks (GANs) have been used to obtain super-resolution (SR) videos that have improved visual perception quality and more coherent details. However, the latest methods perform poorly in areas with dense textures. To better recover the areas with dense textures in video frames and improve the…
Simon Besenthal, Sebastian Maisch, Timo Ropinski
Many lighting methods used in computer graphics such as indirect illumination can have very high computational costs and need to be approximated for real-time applications. These costs can be reduced by means of upsampling techniques which tend to introduce artifacts and affect the visual quality of the rendered image.…
Gonzalez Adriana, Jiang Hong, Gang Huang, Laurent Jacques
We consider the problem of reconstructing an image from compressive measurements using a multi-resolution grid. In this context, the reconstructed image is divided into multiple regions, each one with a different resolution. This problem arises in situations where the image to reconstruct contains a certain region of…
Defne Us, Ulla Ruotsalainen, Sampsa Pursiainen
Background This paper investigates the benefits of data filtering via complex dual wavelet transform for metal artifact reduction (MAR). The advantage of using complex dual wavelet basis for MAR was studied on simulated dental computed tomography (CT) data for its efficiency in terms of noise suppression and removal of…
Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner + 4 more
In this work, we developed multiple 2D and 3D segmentation models with multiresolution input to segment brain tumor components, and then ensembled them to obtain robust segmentation maps. This reduced overfitting and resulted in a more generalized model. Multiparametric MR images of 335 subjects from BRATS 2019…
Shahin Mohammadi, Jose Davila-Velderrain, Manolis Kellis
Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although a large number of methods and approaches exist, robustly identifying underlying cell states and their associations is still a major challenge; given the nonexclusive and dynamic influence of multiple unknown…
Jian Li, Siwang Zhou
Image rescaling (IR) seeks to determine the optimal lowresolution (LR) representation of a high-resolution (HR) image to reconstruct a high-quality super-resolution (SR) image. Typically, HR images with resolutions exceeding 2K possess rich information that is unevenly distributed across the image. Traditional image…
Authors not listed
This conceptual paper introduces the Adaptive Multi-Resolution Modeling Framework (AMRMF), a novel technique designed to revolutionize chemical engineering by integrating multi-scale simulations, quantum-inspired algorithms, advanced uncertainty quantification, and Bayesian inference. The framework bridges theoretical…
Esley Torres García, Raúl Pinto Cámara, Alejandro Linares, Damián Martínez + 22 more
Mean-Shift Super Resolution (MSSR) is a principle based on the Mean Shift theory that improves the spatial resolution in fluorescence images beyond the diffraction limit. MSSR works on low- and high-density fluorophore images, is not limited by the architecture of the detector (EM-CCD, sCMOS, or photomultiplier-based…
Yonatan Kleerekoper, Mohammad Kurtam, Yonatan Keselman, Shai Abramson + 4 more
Functional connectivity (FC) is fundamentally non-stationary, undergoing continuous reconfigurations that track shifting behavioral and cognitive states. Despite the importance of these transitions, existing analytical frameworks struggle to reconcile the high-dimensional nature of these reconfigurations with the need…
Kaicong Sun, Trung-Hieu Tran, Jajnabalkya Guhathakurta, Sven Simon
Multi-image super-resolution (MISR) usually outperforms single-image super-resolution (SISR) under a proper inter-image alignment by explicitly exploiting the inter-image correlation. However, the large computational demand encumbers the deployment of MISR in practice. In this work, we propose a distributed…
Efe Ozturk, Abhijeet Venkataraman, Felix G. Rivera Moctezuma, Ahmet F. Coskun
Mass spectrometry imaging (MSI) is a powerful technique for spatially resolved analysis of metabolites and other biomolecules within biological tissues. However, the inherent low spatial resolution of MSI often limits its ability to provide detailed cellular-level information. To address this limitation, we propose a…
Authors not listed
Hyperstructures and their hierarchical extensions—SuperHyperStructures—provide a versatile algebraic language for modeling multi-level and interdependent systems [1,2]. In materials and chemical sciences, structural descriptions naturally span a broad spectrum of characteristic length scales, commonly organized as…
Chengyi Xie, Jianing Wang, Xin Diao, Xiaoxiao Wang + 1 more
Desorption electrospray ionization mass spectrometry imaging (DESI MSI) is a valuable tool for label-free, spatially resolved molecular analysis of biological tissues. However, its spatial resolution has been limited to tens of micrometers due to constraints in spray geometry and solvent flow, hindering single-cell…
Authors not listed
Scanning emission-based microscopies, such as X-ray fluorescence (XRF) and energy-dispersive X-ray spectroscopy, offer nanometer-scale chemical maps, but suffer from long acquisition times and radiation damage. Lower-flux and shorter dwell time scans mitigate this problem, but the resulting signal loss can only…