19 papers · ranked by Valyu relevance
Yuming Yang, Michael K. Ng, Zhigang Jia, Wei Wang
—In this work, we address the challenging problem of blind deconvolution for color images. Existing methods often convert color images to grayscale or process each color channel separately, which overlooking the relationships between color channels. To handle this issue, we formulate a novel quaternion fidelity term…
Sen Li, Jianchao Wang, Zhanqiang Huo, Roberto Vezzani
Single-image dehazing suffers from severe information loss and the under-constraint problem. The lack of high-quality robust priors leads to limited generalization ability of existing dehazing methods in real-world scenarios. To tackle this challenge, we propose a simple but effective single-image dehazing network by…
Hafiz Tayyab Mustafa, Hamza Mustafa, Hassan Alhuzali, Mujtaba Asad + 2 more
Image fusion is a challenging task that aims to generate a composite image by combining information from diverse sources. While deep learning (DL) algorithms have achieved promising results, most rely on complex encoders or attention mechanisms, leading to high computational cost and potential information loss during…
Joonkoo Park, Shimin Hu
Robust evidence now suggests that numerosity perception emerges from the early visual cortex. However, such empirical findings pose a theoretical challenge for explaining how a low-level perceptual system represents discrete values from continuous input independently of other magnitude dimensions. Among proposals for…
Gunjan Shandilya, Sheifali Gupta, Deepali Gupta, Sapna Juneja + 3 more
The most serious complication of diabetes, Diabetic Foot Ulcer (DFU), can result in chronic infection, damage to tissues, and even amputation if not identified in a timely way. It is even more dangerous for disabled people. Accurate and timely diagnosis is therefore essential for improved patient outcomes. However, it…
Nikolas Markou
We describe and evaluate BF-ConvUNeXt, a compact bias-free ConvNeXt U-Net for blind additive-white-Gaussian-noise color image denoising, combining four existing ingredients so a single property survives end to end: a frozen depthwise Gabor stem (oriented band-pass, zero trainable parameters), a Laplacian-pyramid…
Songjiang Lai, Tsun-Hin Cheung, Ka-Chun Fung, Kaiwen Xue + 7 more
Recent advancements in deep learning have significantly improved fault diagnosis methods. However, challenges such as insufficient feature extraction, limited long-range dependency modeling, and environmental noise continue to hinder their effectiveness. This paper presents a novel mixture of multi-view convolutional…
Kishore Kumar Tarafdar
Deep learning models are widely used to process multidimensional signals such as time series, images, and volumetric medical images, but their learned representations often lack explicit signal structure and are difficult to inspect. This thesis develops model-based, signal-theoretic learning systems guided by data and…
Alexander Auras, Martin Burger, Samira Kabri, Michael Moeller + 1 more
Deep neural networks have shown great empirical success in the solution of a wide variety of ill-posed inverse problems in imaging. Yet, very few works have studied their behavior in the limit that turns the discretized ill-conditioned problems into truly ill-posed ones, i.e., for an increasing resolution of the…
Authors not listed
Metal–organic frameworks (MOFs) represent a versatile class of porous materials, yet efficiently exploring their vast chemical space for target gas adsorption properties remains a major challenge. MOFid, a text-based encoding of MOF structures, has enabled large-scale data mining using natural language processing (NLP)…
Yun Bu, Wenbo Jiang, Gang Lu, Qiang Zhang
When training a neural network, the choice of activation function can greatly impact its performance. A function with a larger derivative may cause the coefficients of the latter layers to deviate further from the calculated direction, making deep learning more difficult to train. However, an activation function with a…
Pei Shi, Qixiang Lu, Jiahui Chen, Xiaoliu Lv + 4 more
Traffic forecasting is crucial for optimizing traffic management and control strategies. As a powerful approach for analyzing and mining graph-structured data, graph convolution has shown great potential in traffic prediction. However, it still struggles to fully capture global spatial correlations and long-term…
Matteo De Matola, Giorgio Arcara
Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come…
Yufei Gao, Jiaqi Li, Jing Xu, Qing Li + 5 more
Accurate and robust classification of medical pathology images is pivotal for computer-aided diagnosis. However, the deployment of deep learning models in high-throughput clinical screening faces a fundamental challenge: the trade-off between diagnostic accuracy and computational efficiency. Current lightweight…
Junseok Lee, Jihye Shin, Sangyong Lee, Chang-Jae Chun
—Rotating bearings play an important role in modern industries, but have a high probability of occurrence of defects because they operate at high speed, high load, and poor operating environments. Therefore, if a delay time occurs when a bearing is diagnosed with a defect, this may cause economic loss and loss of life.…
S. Soniya, K. C. Sriharipriya, J. Christopher Clement, Umashankar Subramaniam
In recent years, Convolutional Neural Networks (CNNs) have achieved remarkable success in various computer vision tasks, including image denoising. Image denoising focuses on reconstructing a clean image from its noise-corrupted counterpart. In this paper, we propose BIRUNet, a bilateral-filter-based noise-residue…
Mingzhe Wei, Pengcheng Xu, Junyu Liu, Xuesong Li + 9 more
Three-dimensional fluorescence microscopy often exhibits anisotropic resolution because axial information is poorly sampled and more blurred than lateral information, which complicates quantitative interpretation of fine 3D structures. Although optical remedies and computational restoration have been explored, many…
Peter Kirchweger, Lev Melnikovsky, Shahar Seifer, Michael Elbaum
Cryo-electron tomography is an expanding technology for the study of macromolecules, viruses, and cells. It is often applied to specimens that are too large or heterogeneous for methods based on 2D image averaging such as single particle analysis, e.g., intracellular membranes or organelles. Current practice records a…
Hayeon Choi, Dasom Im, Sangeun Oh, Jonghwan Lee + 4 more
Wafer map defect classification plays a critical role in yield monitoring and root-cause analysis in semiconductor manufacturing. Although recent convolutional neural network (CNN)-based approaches have achieved high classification accuracy, most existing models are evaluated primarily on clean datasets and remain…