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Works32 from public data
- Multi-scale Residual Network for Image Super-Resolution899
A novel multi-scale residual network (MSRN) to fully exploit the image features, which outperform most of the state-of-the-art methods.
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- Progressive Feature Fusion Network for Realistic Image Dehazing133
An U-Net like encoder-decoder deep network via progressive feature fusions has been proposed to directly learn highly nonlinear transformation function from observed hazy image to haze-free ground-truth and can achieve superior performance when compared with popular state-of-the-art methods.
- MDCN: Multi-Scale Dense Cross Network for Image Super-Resolution124
A Multi-scale Dense Cross Network (MDCN), which achieves great performance with fewer parameters and less execution time and achieves competitive results in SISR, especially in the reconstruction task with multiple upsampling factors.
- AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing93
This paper proposes a new model, called Attention-Augmented Network (AttaNet), to capture both global context and multi-level semantics while keeping the efficiency high, and achieves different levels of speed/accuracy trade-offs on Cityscapes.
- AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models65
This paper proposes the first DDPM-based solution for the problem of atmospheric turbulence mitigation and proposes a fast sampling technique for reducing the inference times for conditional DDPMs.
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- Latent Feature-Guided Diffusion Models for Shadow Removal47
This paper proposes the use of diffusion models as they offer a promising approach to gradually refine the details of shadow regions during the diffusion process, and improves this process by conditioning on a learned latent feature space that inherits the characteristics of shadow-free images, thus avoiding the limitation of conventional methods.
- CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation38
This work in-troduce a novel method, dubbed CoDi, that adapts a pre-trained latent diffusion model to accept additional image conditioning inputs while significantly reducing the sampling steps required to achieve high-quality results.
- LTT-GAN: Looking Through Turbulence by Inverting GANs37
This work proposes a method in which well-trained Generative Adversarial Networks are leveraged as effective priors to mitigate the effect of turbulence, and proposes to connect hierarchical layers of a GAN to get multiple images with the same identity at the same time, which enriches the context and helps to better improve the restoration performance.
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- Bigger is not Always Better: Scaling Properties of Latent Diffusion Models29
An in-depth investigation into how model size influences sampling efficiency across varying sampling steps unveil a surprising trend: when operating under a given inference budget, smaller models frequently outperform their larger equivalents in generating high-quality results.
- HighEr-Resolution Network for Image Demosaicing and Enhancing29
A HighEr-Resolution Network (HERN) to fully learning global information in high-resolution image patches by combining global-aware features and multi-scale features, which achieves state-of-the-art performance on the AIM2019 RAW to RGB mapping challenge.
- The Power of Context: How Multimodality Improves Image Super-Resolution19
This work proposes a novel approach that leverages the rich contextual information available in multiple modalities to learn a powerful generative prior for SISR within a diffusion model framework, and mitigate hallucinations, often introduced by text prompts, by using spatial information from other modalities to guide regional text-based conditioning.
- Streaming Autoregressive Video Generation via Diagonal Distillation18
Diagonal Distillation is proposed, which operates orthogonally to existing approaches and better exploits temporal information across both video chunks and denoising steps and mitigates error propagation and reduces oversaturation in long-range sequences.
- An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks17
An effective deep residual network based on squeeze-and-excitation blocks (SEBlock) to reconstruct high-resolution (HR) image from low- resolution (LR) image and get finer texture details.
- A Comparison of Different Atmospheric Turbulence Simulation Methods for Image Restoration11
This paper systematically evaluates the effectiveness of various turbulence simulation methods on image restoration and provides guidance to the researchers and practitioners working in this field to choose the suitable data generation models for training deep models for turbulence mitigation.
- Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors8
A new method is introduced that brings the predicted samples to the training data manifold using a pretrained unconditional diffusion model that acts as a regularizer and reduces the divergence introduced by the conditional model at each sampling step.
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- Deep residual refining based pseudo‐multi‐frame network for effective single image super‐resolution8
A deep residual refining based pseudo-multi-frame network for efficient SISR that could make full use of available redundant information possessed in hierarchical layers and can outperform most of the state-of-the-art methods with acceptable less parameters.
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- Thermal to Visible Image Synthesis Under Atmospheric Turbulence6
This is the first work that studies the problem of thermal to visible image translation under atmospheric turbulence and an end-to-end reconstruction method is proposed which can directly transform thermal images into visible-spectrum images by utilizing natural image priors based on a pre-trained StyleGAN2 network.
- Reference-Guided Identity Preserving Face Restoration4
A novel approach that maximizes reference face utility for improved face restoration and identity preservation and achieves state-of-the-art identity preserving restoration on benchmarks such as FFHQ-Ref and CelebA-Ref-Test, consistently outperforming previous work.
- SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning2
SymbOmni is proposed, an agentic omni-model designed for cumulative evolution through Symbolic Concept Learning that significantly outperforms existing agent-based systems for iterative creation and also surpasses closed-source models in both image quality and task success rates.
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- T1: Scaling Diffusion Probabilistic Fields to High-Resolution on Unified Visual Modalities1
This work proposes a new model comprising of a view-wise sampling algorithm to focus on local structure learning, and incorporating additional guidance, e.g., text description, to complement the global geometry.
- SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom1
A Squared Deformable Alignment Network (SDAN) is introduced to address the issue of misalignment in super-resolution algorithms, and leverages the packing and unpacking operations to enlarge the receptive field of the offset learning and to enhance the ability of extracting the spatial connection between the low-resolution images and the referenced images.
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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 2026-10-15. 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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