Complex-Valued
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Related Articles from SNS
PolSAR Image Classification using a Hybrid Complex-Valued Network (HybridCVNet)
arXiv:2605.31137v1 Announce Type: new Abstract: Recently, convolutional neural networks (CNNs) have become popular for image classification due to their effectiveness in computer vision tasks. Now, researchers are exploring the potential of vision transformers (ViTs) in remote sensing and Earth observation. However, traditional Real-Valued networks often overlook important phase information in Complex-Valued (CV) data like polarimetric synthetic aperture radar (PolSAR) data.
Data Efficient Complex Feature Fusion Network For Hyperspectral Image Classification
arXiv:2606.04710v1 Announce Type: new Abstract: This work presents a data-efficient variant of the Attention-Based Dual-Branch Complex Feature Fusion Network (CFFN) for hyperspectral image classification. The proposed model, termed DE-CFFN, retains the original two-stream structure: the Real-Valued Neural Network (RVNN) processes standard hyperspectral patches, while the Complex-Valued Neural Network (CVNN) handles their Fourier-transformed counterparts.
On the conditional equivalence of phase retrieval algorithms
Announce Type: cross Abstract: Phase retrieval - recovering a complex-valued field from intensity measurements - is typically solved using variants of the Gerchberg-Saxton (GS) algorithm, understood as alternating projections between measurement planes. Meanwhile, modern computational imaging increasingly relies on gradient-based optimization and automatic differentiation.
Boundedness of Left Half-Plane Eigenvalues for Non-Selfadjoint Indefinite Sturm--Liouville Problems with Applications to Fourier Modal Methods
arXiv:2606.03537v1 Announce Type: cross Abstract: We study a general class of non-selfadjoint indefinite Sturm--Liouville problems of the form $$ -(p\,y')' q\,y = \lambda\, p\, y, $$ on a finite interval with complex-valued coefficients, where $p$ is piecewise in $W^{2,\infty}$, non-vanishing, and satisfies a non-degenerate interface condition, and $q$ is bounded. We prove that all eigenvalues in the open left half-plane are contained in a bounded set, which, by classical Sturm--Liouville...
On the conditional equivalence of phase retrieval algorithms
Announce Type: new Abstract: Phase retrieval - recovering a complex-valued field from intensity measurements - is typically solved using variants of the Gerchberg-Saxton (GS) algorithm, understood as alternating projections between measurement planes. Meanwhile, modern computational imaging increasingly relies on gradient-based optimization and automatic differentiation. Here we show that these two approaches are mathematically identical: the GS magnitude replacement step is exactly a unit...
Boundedness of Left Half-Plane Eigenvalues for Non-Selfadjoint Indefinite Sturm--Liouville Problems with Applications to Fourier Modal Methods
arXiv:2606.03537v1 Announce Type: new Abstract: We study a general class of non-selfadjoint indefinite Sturm--Liouville problems of the form $$ -(p\,y')' q\,y = \lambda\, p\, y, $$ on a finite interval with complex-valued coefficients, where $p$ is piecewise in $W^{2,\infty}$, non-vanishing, and satisfies a non-degenerate interface condition, and $q$ is bounded. We prove that all eigenvalues in the open left half-plane are contained in a bounded set, which, by classical Sturm--Liouville...
Binary Amplitude Modulation Suppresses Noise Up-Conversion in Coherent Diffractive Optical Networks
arXiv:2605.30820v1 Announce Type: new Abstract: We establish a fundamental principle in coherent wave-optical computing: restricting the modulation manifold from continuous complex-valued to binary amplitude suppresses stochastic-noise up-conversion while preserving classification fidelity, yielding a counter-intuitive less-is-more robustness law. Seven-layer binary-amplitude-mask D2NN (BM-D2NN) achieve 90.9% (MNIST) and 81.9% (Fashion-MNIST) test accuracy, within 2~4 pp of...
Parallel Complex Diffusion for Scalable Time Series Generation
Announce Type: replace Abstract: Diffusion models learn data distributions indirectly through denoising, making the difficulty of generative modeling closely tied to the dependency structure of data. For time series, strong temporal dependence forces the noise / score estimator to recover highly entangled cross-time relationships, leading to the curse of entanglement. We mitigate this burden by changing the topology of the diffusion space: the Discrete Fourier Transform (DFT) decomposes...
SDF2Net: Shallow to Deep Feature Fusion Network for PolSAR Image Classification
arXiv:2402.17672v2 Announce Type: replace Abstract: Polarimetric synthetic aperture radar (PolSAR) images encompass valuable information that can facilitate extensive land cover interpretation and generate diverse output products. Extracting meaningful features from PolSAR data poses challenges distinct from those encountered in optical imagery. Deep learning (DL) methods offer effective solutions for overcoming these challenges in PolSAR feature extraction.
Deep Psychovisual Image Representations
Announce Type: replace Abstract: Psychovisual models suggest human vision decouples low-level feature extraction from higher cognition by first forming intermediate abstractions. In contrast, deep learning-based vision models routinely extract and aggregate features using homogeneous stacks of spatial layers, rendering their decision-making processes opaque. In this paper, we propose Deep Visual Coding, a learned frequency-domain representation inspired by 1990s image codes that quantised...