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Works29 from public data
- Complexity Experts are Task-Discriminative Learners for Any Image Restoration128
The proposed MoCE-IR model outperforms state-of-the-art methods, affirming its efficiency and practical applicability and introducing “complexity experts” – flexible expert blocks with varying computational complexity and receptive fields.
- The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report91
This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes, and gauge the state-of-the-art in efficient single-image super-resolution.
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- See More Details: Efficient Image Super-Resolution by Experts Mining51
This work introduces SeemoRe, an efficient SR model employing expert mining, strategically incorporates experts at different levels, adopting a collaborative methodology, allowing the model to achieve an optimal performance with minimal computational costs in efficient settings.
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- MSAR-Net: Multi-scale attention based light-weight image super-resolution24
A Light-weight multi-scale attention residual network (MSAR-Net) for SISR, designed in progressive fashion, for substituting the large scale factors with small scale factor combinations, and thus gradually exploit the hierarchical information.
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- Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement21
A novel Gated Multi-Resolution Transfer Network (GMTNet) is proposed to reconstruct a spatially precise high-quality image from a burst of low-quality raw images and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement.
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- Efficient Degradation-aware Any Image Restoration12
Extensive comparisons demonstrate that the DaAIR outperforms both state-of-the-art All-in-One models and degradation-specific counterparts, affirming the efficacy and practicality of this efficient restorer integrating holistic and specific learning within a unified model.
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- Color Matching Using Hypernetwork-Based Kolmogorov-Arnold Networks6
A hypernetwork is developed that generates spatially varying weight maps to control the nonlinear splines of a KAN, enabling accurate color matching and outperforms existing approaches while remaining lightweight compared to other methods.
- ContextFormer: Redefining Efficiency in Semantic Segmentation5
This work proposes ContextFormer, a hybrid framework leveraging the strengths of CNNs and ViTs in the bottleneck to balance efficiency, accuracy, and robustness for real-time semantic segmentation, setting a new benchmark for efficiency and performance.
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- AdaptSR: Low-Rank Adaptation for Efficient and Scalable Real-World Super-Resolution3
This work introduces AdaptSR, a low-rank adaptation (LoRA) framework that efficiently repurposes bicubic-trained SR models for real-world tasks and matches or exceeds full model fine-tuning while training 92% fewer parameters, enabling rapid adaptation to real SR tasks within minutes.
- DiTVR: Zero-Shot Diffusion Transformer for Video Restoration3
DiTVR is introduced, a zero shot video restoration framework that couples a diffusion transformer with trajectory aware attention and a wavelet guided, flow consistent sampler, demonstrating superior temporal consistency and detail preservation while remaining robust to flow noise and occlusions.
- AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results3
This paper thoroughly evaluates the state-of-the-art advances in high-FPS single image motion deblurring, showcasing the significant progress in the field, while leveraging samples of the novel dataset, MIORe, that introduces challenging examples of movement patterns.
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Publication data from OpenAlex; 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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