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- Ntire 2025 Image Shadow Removal Challenge Report22
This work examines the findings of the NTIRE 2025 Shadow Removal Challenge, with 17 teams successfully submitting their solutions during the final evaluation phase, and one focusing on reconstruction fidelity and the other on visual perception through a user study.
- The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview20
A review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the resulting outcomes and offering an in-depth overview of the latest trends in the field.
- DreamUHD: Frequency Enhanced Variational Autoencoder for Ultra-High-Definition Image Restoration17
This work introduces a wavelet-based adapter that extracts multi-scale image information and employs frequency-aware adaptive modulation to bridge the domain gap by integrating degraded image data into the pre-trained VAE.
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- Temporal-Synergistic Policy Optimization for Unsupervised Low-Light Image Enhancement–
This work proposes an unsupervised low-light enhancement framework based on Group Relative Policy Optimization (GRPO), which utilizes perceptual preferences to directly optimize the diffusion policy and introduces a Sliding Window Hybrid ODE-SDE Sampling strategy that confines stochasticity to dynamic sub-intervals, thereby achieving efficient coarse-to-fine exploration and precise advantage attribution.
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- Geometry-Aware Enhancement and Data Augmentation for Street-to-Satellite Geo-localization–
A lightweight and geometry-aware framework that enhances front-view street images via a calibration-free spherical transform that projects single-view images into a bird's-eye view (BEV), effectively aligning their perspective with satellite imagery.
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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 2026-10-16. 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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