UniProcessor: A Text-induced Unified Low-level Image Processor
Huiyu Duan, Xiongkuo Min, Sijing Wu, Wei Shen, Guangtao Zhai

TL;DR
UniProcessor is a versatile, text-controlled image processing model that handles multiple degradation types and levels without retraining, outperforming existing methods and enabling individual distortion handling in complex images.
Contribution
The paper introduces UniProcessor, a unified low-level image processor controlled by text prompts, capable of handling diverse degradations and levels without additional training.
Findings
Processes 30 degradation types effectively
Outperforms existing methods in quality and flexibility
Handles multiple degradations and individual distortions simultaneously
Abstract
Image processing, including image restoration, image enhancement, etc., involves generating a high-quality clean image from a degraded input. Deep learning-based methods have shown superior performance for various image processing tasks in terms of single-task conditions. However, they require to train separate models for different degradations and levels, which limits the generalization abilities of these models and restricts their applications in real-world. In this paper, we propose a text-induced unified image processor for low-level vision tasks, termed UniProcessor, which can effectively process various degradation types and levels, and support multimodal control. Specifically, our UniProcessor encodes degradation-specific information with the subject prompt and process degradations with the manipulation prompt. These context control features are injected into the UniProcessor…
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Taxonomy
TopicsMedical Image Segmentation Techniques · Image Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques
