As an alternative to acquiring high-resolution hyperspectral images (HR-HSI), Hyperspectral Image Fusion (HIF) aims to recover clean HR-HSIs by fusing degraded low spatial resolution hyperspectral images and high spatial resolution multispectral images. Among existing HIF approaches, model-guided HIF methods stand out by integrating physical degradation constraints with the learning capabilities of data-driven networks. However, most of them learn deep priors only from degraded-clean pairs without degradation-free knowledge, making them struggle with severe or unseen degradations. To address these issues, we propose a Vector-Quantized Prior-Guided Network (VPG-Net), an unfolding-based HIF framework enhanced by sparse representation and novel uncertainty-driven generative priors. Specifically, VPG-Net unfolds the Maximum A Posteriori (MAP) estimation with a sparse representation model into an uncertainty-aware VQ prior-guided network implementation. Within this framework, the sparse representation prior is integrated into the MAP formulation to improve noise resistance. As the core of our method, we leverage a high-quality vector-quantized (VQ) prior, which serves as a powerful degradation-free generative prior for the HIF process. We pre-train a discrete codebook and encoder on clean HR-HSIs to generate a VQ-prior representation (VQPR), which preserves complete spatial-spectral information. To effectively bridge the gap between degraded inputs and the learned degradation-free codebook, we further incorporate a novel uncertainty-driven probabilistic matching strategy that improves feature alignment and suppresses artifacts. The learned VQPR is then incorporated into the deep prior module as dynamic modulation parameters to enhance the fidelity and realism of the reconstructed results, particularly for severely degraded inputs. Extensive experiments on clean and degraded synthetic and real-world datasets demonstrate that our approach outperforms state-of-the-art HIF methods in both quantitative metrics and visual quality.

Although existing diffusion-based image super-resolution methods have achieved remarkable visual quality, they often struggle with fidelity issues, particularly in preserving consistency with the original input image. This issue arises because using low-quality images as conditional inputs introduces substantial errors in the diffusion backward denoising process, making the restored features deviate from target features and thus degrade image fidelity. To improve the accuracy of noise estimation, we propose a dual-memory module to reinforce the input low-quality conditional features, which consists of a pre-trained high-quality memory bank to enrich the structural information and a degradation memory to remove the degradation components. Furthermore, we develop an uncertainty-aware noise estimation framework, utilizing an extra branch in the denoising network to predict pixel-wise uncertainty values, thus dynamically adjust the optimization weights for high-uncertainty regions. This adaptive strategy effectively improves the accuracy of noise estimation in challenging reconstruction areas. Experimental results demonstrate that our method significantly enhances the fidelity while preserving high visual quality of diffusion-based super-resolution, improving the reliability of diffusion applications.

Network pruning is a widely studied technique of obtaining compact representations from over-parameterized deep convolutional neural networks . Existing pruning methods are based on finding an optimal combination of pruned filters in the fixed search space . However, the optimality of those methods is often questionable due to limited search space and pruning choices - e.g., the difficulty with removing the entire layer and the risk of unexpected performance degradation . Inspired by the exploration vs. exploitation trade-off in reinforcement learning, we propose to reconstruct the filter space without increasing the model capacity and prune them by exploiting group sparsity . Our approach challenges the conventional wisdom by advocating the strategy of Growing-before-Pruning (GbP), which allows us to explore more space before exploiting the power of architecture search. Meanwhile, to achieve more efficient pruning, we propose to measure the importance of filters by global group sparsity , which extends the existing Gaussian scale mixture model. Such global characterization of sparsity in the filter space leads to a novel deterministic annealing strategy for progressively pruning the filters. We have evaluated our method on several popular datasets and network architectures. Our extensive experiment results have shown that the proposed method advances the current state-of-the-art.

Many deep learning-based solutions to blind image deblurring estimate the blur representation and reconstruct the target image from its blurry observation. However, these methods suffer from severe performance degradation in real-world scenarios because they ignore important prior information about motion blur (e.g., real-world motion blur is diverse and spatially varying). Some methods have attempted to explicitly estimate non-uniform blur kernels by CNNs, but accurate estimation is still challenging due to the lack of ground truth about spatially varying blur kernels in real-world images. To address these issues, we propose to represent the field of motion blur kernels in a latent space by normalizing flows, and design CNNs to predict the latent codes instead of motion kernels. To further improve the accuracy and robustness of non-uniform kernel estimation, we introduce uncertainty learning into the process of estimating latent codes and propose a multi-scale kernel attention module to better integrate image features with estimated kernels. Extensive experimental results, especially on real-world blur datasets, demonstrate that our method achieves state-of-the-art results in terms of both subjective and objective quality as well as excellent generalization performance for non-uniform image deblurring. The code is available at https://see.xidian.edu.cn/faculty/wsdong/Projects/UFPNet.htm.