الباحثون

Xiangtao Kong

المنشورات 3

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HarnessIR: Harnessing Multimodal Foundation Models for Universal Real-World Image Restoration

Xiangtao Kong, Shuaizheng Liu, Rongyuan Wu وآخرون · 2026

Real-world low-quality images suffer from complex mixed degradations, including but not limited to noise, blur, atmospheric effects, etc. Recent agentic methods usually model real-world image restoration (Real-IR) as a sequential tool calling problem over task-specific single-degradation restoration models. This paradi …

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LDM-is-AE: Latent Diffusion Model is an Auto-Encoder for End-to-End Image Generation

Zhengqiang Zhang, Lingchen Sun, Rongyuan Wu وآخرون · 2026

Latent Diffusion Models (LDMs) typically adopt a two-stage pipeline: an auto-encoder (AE) is first pre-trained to define a latent space, then a diffusion model is trained to perform denoising within it. Such a two-stage design introduces a representation mismatch, as the latent space is optimized for reconstruction rat …

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PIC-UIE: Predicting Image-Adaptive Corrections for Lightweight Underwater Image Enhancement

Cunhao Zhu, Dongliang Xu, Xiangtao Kong وآخرون · 2026

Underwater image enhancement (UIE) aims to restore visibility, color fidelity, and structural detail from images degraded by wavelength-dependent attenuation and backscatter. State-of-the-art UIE methods often rely on large backbones and dense image-to-image prediction, limiting their practicality for edge deployment. …

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