TL;DR
This paper proposes a scalable framework for image restoration and enhancement built upon the reinforcement learning (RL) paradigm, and confirms the model’s superior few-shot and zero-shot capabilities compared to existing methods, as well as its flexibility in addressing multiple competing objectives.
MGN-AIR is presented, a novel pixel-level restoration framework for all-in-one image restoration that leverages both textual and visual prompts to provide global and local degradation cues, guiding the model on where to look and how to restore at each pixel.
A novel diffusion framework that employs interactive modeling of determinism and stochasticity inherent in multiple image degradation processes, enabling it to handle diverse IR scenarios and demonstrating excellent generality and adaptability.
Real images often exhibit unknown and mixed degradations, making restoration substantially more challenging than single-task image restoration because multiple distortion types interact within the same observation. Consequently, existing methods often rely on prior knowledge of the degradation type or separate task-spe...
This work proposes PCFlow (Perceptually Consistent Flow Matching), a unified framework that directly parameterizes a continuous transport from degraded observations to clean targets, jointly optimizing distortion and perceptual quality and integrates a conflict-free gradient projection strategy to stabilize the multi-o...
GraLoD, a plug-and-play framework that treats restoration scale as a spatially varying and stage-dependent continuous variable, is proposed and minimal-sufficient footprint calibration (MSFC) together with structure-aware regularization (SAR) is introduced to encourage restoration-effective and spatially coherent LOD a...
This paper systematically reviews existing AiOIR techniques and provides an in-depth analysis of their innovations in architectural evolution and feature learning paradigms, and critically evaluates the bottlenecks and challenges encountered by current models.
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