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Works18 from public data
- Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild372
This work introduces SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up, and develops a restoration-guided sampling method to suppress the fidelity issue encountered in generative-based restoration.
- Fusformer: A Transformer-Based Fusion Network for Hyperspectral Image Super-Resolution252
This letter designs a Transformer-based architecture (called Fusformer) for the HISR problem, which is the first attempt to apply the Transformer architecture to this task to the best of the authors' knowledge.
- Hyperspectral Image Super-Resolution via Deep Spatiospectral Attention Convolutional Neural Networks239
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- A Deep-Shallow Fusion Network With Multidetail Extractor and Spectral Attention for Hyperspectral Pansharpening54
A new hyperspectral pansharpening network architecture (called Hyper-DSNet) is developed to fully preserve latent spatial details and spectral fidelity via a deep-shallow fusion structure with multi-detail extractor and spectral attention to preserve the spectrum for a wealth of spectral information of HS images.
- Harnessing Diffusion-Yielded Score Priors for Image Restoration45
HYPIR outperforms previous state-of-the-art methods, achieving efficient and high-quality image restoration, and inherits the capabilities of diffusion models with rich user control, enabling text-guided restoration and adjustable texture richness.
- A New Variational Approach Based on Proximal Deep Injection and Gradient Intensity Similarity for Spatio-Spectral Image Fusion42
The proposed convex VO problem is efficiently solved by exploiting the framework of the alternating direction method of multipliers (ADMM), thus guaranteeing the convergence of the algorithm.
- Source-Adaptive Discriminative Kernels based Network for Remote Sensing Pansharpening41
ADKNet outperforms current state-of-the-art (SOTA) pansharpening methods in both quantitative and qualitative assessments, and is extended to the hyperspectral image super-resolution (HSISR) problem, still yields SOTA performance, proving the universality of the model.
- Interpreting Low-Level Vision Models With Causal Effect Maps20
This work introduces causality theory to interpret low-level vision models and proposes a model-/task-agnostic method called Causal Effect Map (CEM), which can visualize and quantify the input-output relationships on either positive or negative effects.
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- UniCon: Unidirectional Information Flow for Effective Control of Large-Scale Diffusion Models5
UniCon implements a unidirectional flow from the diffusion network to the adapter, allowing the adapter alone to generate the final output, reducing computational demands by eliminating the need for the diffusion model to compute and store gradients during adapter training.
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- Revisiting the generalization problem of low-level vision models through the lens of image deraining–
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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