# A General Decoupled Learning Framework for Parameterized Image Operators

arXiv: https://arxiv.org/abs/1907.05852 · HTML: https://mlsanity.com/paper/1907.05852/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2019.

## Built on by (most cited first, 30 of 72)

- [InstructIR: High-Quality Image Restoration Following Human Instructions](https://mlsanity.com/paper/2401.16468/) (2024)
- [PromptIR: Prompting for All-in-One Blind Image Restoration](https://mlsanity.com/paper/2306.13090/) (2023)
- [AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation](https://mlsanity.com/paper/2403.14614/) (2024)
- [Complexity Experts are Task-Discriminative Learners for Any Image Restoration](https://mlsanity.com/paper/2411.18466/) (2024)
- [Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model](https://mlsanity.com/paper/2403.11157/) (2024)
- [Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration](https://mlsanity.com/paper/2408.15994/) (2024)
- [A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends](https://mlsanity.com/paper/2410.15067/) (2024)
- [Prompt-In-Prompt Learning for Universal Image Restoration](https://mlsanity.com/paper/2312.05038/) (2023)
- [Degradation-Aware Feature Perturbation for All-in-One Image Restoration](https://mlsanity.com/paper/2505.12630/) (2025)
- [Improving Image Restoration through Removing Degradations in Textual Representations](https://mlsanity.com/paper/2312.17334/) (2023)
- [Textual Prompt Guided Image Restoration](https://mlsanity.com/paper/2312.06162/) (2023)
- [Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement](https://mlsanity.com/paper/2112.05147/) (2021)
- [Prompt-based Ingredient-Oriented All-in-One Image Restoration](https://mlsanity.com/paper/2309.03063/) (2023)
- [UniProcessor: A Text-induced Unified Low-level Image Processor](https://mlsanity.com/paper/2407.20928/) (2024)
- [All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation](https://mlsanity.com/paper/2308.03021/) (2023)
- [Degradation-Aware Residual-Conditioned Optimal Transport for Unified Image Restoration](https://mlsanity.com/paper/2411.01656/) (2024)
- [Restore Anything with Masks: Leveraging Mask Image Modeling for Blind All-in-One Image Restoration](https://mlsanity.com/paper/2409.19403/) (2024)
- [Multi-Expert Adaptive Selection: Task-Balancing for All-in-One Image Restoration](https://mlsanity.com/paper/2407.19139/) (2024)
- [LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image Restoration](https://mlsanity.com/paper/2410.15385/) (2024)
- [Multi-task Image Restoration Guided By Robust DINO Features](https://mlsanity.com/paper/2312.01677/) (2023)
- [UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity](https://mlsanity.com/paper/2412.20157/) (2024)
- [Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image Restoration](https://mlsanity.com/paper/2405.02843/) (2024)
- [HAIR: Hypernetworks-based All-in-One Image Restoration](https://mlsanity.com/paper/2408.08091/) (2024)
- [ClearAIR: A Human-Visual-Perception-Inspired All-in-One Image Restoration](https://mlsanity.com/paper/2601.02763/) (2026)
- [Efficient Degradation-aware Any Image Restoration](https://mlsanity.com/paper/2405.15475/) (2024)
- [Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image Restoration](https://mlsanity.com/paper/2404.02154/) (2024)
- [Efficient Degradation-agnostic Image Restoration via Channel-Wise Functional Decomposition and Manifold Regularization](https://mlsanity.com/paper/2505.18679/) (2025)
- [PromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation](https://mlsanity.com/paper/2411.15922/) (2024)
- [DSwinIR: Rethinking Window-based Attention for Image Restoration](https://mlsanity.com/paper/2504.04869/) (2025)
- [MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Prediction](https://mlsanity.com/paper/2111.05055/) (2021)

## Abstract (arXiv)

Many different deep networks have been used to approximate, accelerate or improve traditional image operators. Among these traditional operators, many contain parameters which need to be tweaked to obtain the satisfactory results, which we refer to as parameterized image operators. However, most existing deep networks trained for these operators are only designed for one specific parameter configuration, which does not meet the needs of real scenarios that usually require flexible parameters settings. To overcome this limitation, we propose a new decoupled learning algorithm to learn from the operator parameters to dynamically adjust the weights of a deep network for image operators, denoted as the base network. The learned algorithm is formed as another network, namely the weight learning network, which can be end-to-end jointly trained with the base network. Experiments demonstrate that the proposed framework can be successfully applied to many traditional parameterized image operators. To accelerate the parameter tuning for practical scenarios, the proposed framework can be further extended to dynamically change the weights of only one single layer of the base network while sharing most computation cost. We demonstrate that this cheap parameter-tuning extension of the proposed decoupled learning framework even outperforms the state-of-the-art alternative approaches.