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Works233 from public data
- Image Super-Resolution Using Very Deep Residual Channel Attention Networks5,700
This work proposes a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections, and proposes a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels.
- Residual Dense Network for Image Super-Resolution4,162
This paper proposes residual dense block (RDB) to extract abundant local features via dense connected convolutional layers and uses global feature fusion in RDB to jointly and adaptively learn global hierarchical features in a holistic way.
- 1,955
- CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion990
A novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network that achieves promising results in multiple fusion tasks, including infrared- visible image fusion and medical image fusion, and can boost the performance in downstream infrared-visible semantic segmentation and object detection in a unified benchmark.
- Residual Dense Network for Image Restoration895
This work proposes residual dense block (RDB) to extract abundant local features via densely connected convolutional layers and proposes local feature fusion in RDB to adaptively learn more effective features from preceding and current local features and stabilize the training of wider network.
- Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement881
A simple yet principled One-stage Retinex-based Framework (ORF), designed an Illumination-Guided Transformer (IGT) that utilizes illumination representations to direct the modeling of non-local interactions of regions with different lighting conditions, and obtains the algorithm, Retinexformer.
- TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution655
Experimental results demonstrate that theTDAN is capable of alleviating occlusions and artifacts for temporal alignment and the TDAN-based VSR model outperforms several recent state-of-the-art VSR networks with a comparable or even much smaller model size.
- GAN Inversion: A Survey653
This paper provides a survey of GAN inversion with a focus on its representative algorithms and its applications in image restoration and image manipulation, and discusses the trends and challenges for future research.
- Visual Semantic Reasoning for Image-Text Matching625
A simple and interpretable reasoning model to generate visual representation that captures key objects and semantic concepts of a scene that outperforms the current best method for image retrieval and caption retrieval on MS-COCO and Flickr30K datasets.
- DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion480
This work proposes a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM) that can generate high-quality fused images with natural image generative priors and cross-modality information from source images.
- DiffIR: Efficient Diffusion Model for Image Restoration476
This work proposes an efficient DM for IR (DiffIR), which consists of a compact IR prior extraction network (CPEN), dynamic IR transformer (DIRformer), and denoising network, which can adopt a joint optimization of CPENS2, DIRformer, and denoising network, which can further reduce the estimation error influence.
- Dual Aggregation Transformer for Image Super-Resolution454
A novel Transformer model, Dual Aggregation Transformer (DAT), is proposed, which aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner.
- Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction423
A novel framework, Mask-guided Spectral-wise Transformer (MST), that treats each spectral feature as a token and calculates self-attention along the spectral dimension and significantly outperforms state-of-the-art (SOTA) methods on simulation and real HSI datasets while requiring dramatically cheaper computational and memory costs.
- 405
- LatticeNet: Towards Lightweight Image Super-Resolution with Lattice Block367
A lightweight SR model, LatticeNet, is proposed, which uses series connection of LBs and the backward feature fusion, which can achieve superior accuracy on four available benchmark datasets against other state-of-the-art methods, while maintaining relatively low computation and memory requirements.
- MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction364
This work proposes a novel Transformer-based method, Multi-stage Spectral-wise Transformer (MST++), for efficient spectral reconstruction that significantly outperforms other state-of-the-art methods.
- Adversarial Feature Hallucination Networks for Few-Shot Learning324
Adversarial Feature Hallucination Networks (AFHN) is proposed which is based on conditional Wasserstein Generative Adversarial networks (cWGAN) and hallucinates diverse and discriminative features conditioned on the few labeled samples and comparative results substantiate the superiority of AFHN to existing data augmentation based FSL approaches and other state-of-the-art ones.
- 307
- Equivariant Multi-Modality Image Fusion299
This work proposes the Equivariant Multi-Modality imAge fusion (EMMA) paradigm, a novel training paradigm that encompasses a fusion module, a pseudo-sensing module, and an equivariant fusion module that enables the net training to follow the principles of the natural sensing-imaging process while satisfying the equivariant imaging prior.
- Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis270
Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability.
- HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging250
A high-resolution dual-domain learning network (HDNet) for HSI reconstruction with HR pixel-level attention and frequency-level refinement that achieves SOTA performance on simulated and real HSI datasets.
- MambaIRv2: Attentive State Space Restoration229
This work proposes MambaIRv2, which equips Mamba with the non-causal modeling ability similar to ViTs to reach the attentive state space restoration model, and introduces a semantic-guided neighboring mechanism to encourage interaction between distant but similar pixels.
- Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution214
A one-stage space-time video super-resolution framework is proposed, which directly synthesizes an HR slow-motion video from an LFR, LR video and proposes a deformable ConvLSTM to align and aggregate temporal information simultaneously for better leveraging global temporal contexts.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. 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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