TL;DR
A novel deep unfolding network that combines Fourier-domain closed-form solvers with a Cascaded Residual Network (FDCRNet) is proposed, offering a promising solution for real-world blind SR.
Real images often exhibit unknown and mixed degradations, making restoration substantially more challenging than single-task image restoration because multiple distortion types interact within the same observation. Consequently, existing methods often rely on prior knowledge of the degradation type or separate task-spe...
This work proposes a perceptually regularized diffusion framework that incorporates prior knowledge through perceptual-loss-based regularization, improving training convergence and encouraging the recovery of meaningful image features.
A novel cross-domain DL framework for MRI reconstruction that leverages graph-based convolutional neural networks to model the autoregressive nature of Fourier features and integrates spatial domain networks and FDNs through a cross-lattice structure, enhancing feature extraction by promoting dense representation throu...
Radio map estimation (RME), also known as spectrum cartography, aims to recover radio signal strength across multiple domains (e.g., spatial and spectral domains) from sparse measurements, naturally leading to a tensor completion problem. Recent advances show that deep learning-based generative models can provide data-...
Deep unfolding networks (DUNs) offer an iterative paradigm that unrolls optimization procedures into a cascaded network structure for hyperspectral image (HSI) denoising. However, existing DUNs for HSI denoising suffer from two notable limitations: 1) the ill-posed inverse problem of handling severely degraded observat...
Image super-resolution (SR), which aims to reconstruct a high-resolution image from a low-resolution input, has progressed from convolutional neural networks (CNNs) to transformer-based architectures. Despite this progress, lightweight transformer SR remains challenging: local or window-based operations provide limited...
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