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- MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution199
The MASA network for RefSR is proposed, where two novel modules are designed to address the problems of high-quality correspondence matching and the computational cost by a coarse-to-fine correspondence matching scheme.
- Video Frame Interpolation with Transformer143
A novel framework is introduced, which takes advantage of Transformer to model long-range pixel correlation among video frames and is equipped with a novel cross-scale window-based attention mechanism, where cross- scale windows interact with each other.
- Best-Buddy GANs for Highly Detailed Image Super-resolution95
This work considers the single image super-resolution (SISR) problem, and proposes best-buddy GANs (Beby-GAN) for rich-detail SISR, a region-aware adversarial learning strategy that directs the model to focus on generating details for textured areas adaptively.
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- Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising7
This paper shows that a naive application of conventional diffusion models is inadequate for this task and proposes three key adaptations that enable high-precision noise generation: a two-branch architecture to better model signal-dependent and signal-independent noise, the incorporation of positional information to capture fixed-pattern noise, and a tailored diffusion noise schedule.
- Blind Face Restoration under Extreme Conditions: Leveraging 3D-2D Prior Fusion for Superior Structural and Texture Recovery7
This paper shows that utilizing 3D priors effectively compensates for structure knowledge deficiencies in 2D priors while preserving the texture details within the restored images, and introduces FREx (Face Restoration under Extreme conditions), a model that combines structure-accurate 3D priors and texture-rich 2D priors in pretrained generative networks for blind face restoration under extreme conditions.
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- A Traffic Camera Calibration Method Based on Multi-rectangle3
A calibration method based on multi-rectangle, which constructs several rectangles with the mark lines of the traffic road to establish the video image distance conversion model and is more accurate when compared with the mainstream methods.
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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 2026-10-14. 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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