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RL-I2IT: Image-to-image translation with deep reinforcement learning.

Jing Hu1, Ziwei Luo2, Chengming Feng1

  • 1School of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan, 610225, China.

Neural Networks : the Official Journal of the International Neural Network Society
|November 11, 2025
PubMed
Summary

This study introduces a novel Reinforcement Learning-based Image-to-Image Translation (RL-I2IT) framework. It efficiently decomposes complex image translation into iterative steps using a lightweight model, improving performance and reducing overfitting.

Keywords:
Deep learningDeep reinforcement learningGenerative modelImage to image translationMeta policy

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Existing Image-to-Image Translation (I2IT) methods often use single-step deep learning (DL) models, which can be parameter-heavy and prone to overfitting.
  • Designing efficient and robust I2IT models remains a challenge, particularly for high-dimensional continuous state and action spaces.

Purpose of the Study:

  • To reformulate Image-to-Image Translation (I2IT) as an iterative decision-making problem using deep reinforcement learning (DRL).
  • To propose a computationally efficient RL-based I2IT (RL-I2IT) framework that decomposes monolithic learning into smaller, manageable steps.
  • To address the challenges of high-dimensional continuous state and action spaces in DRL for I2IT.

Main Methods:

  • The proposed RL-I2IT framework utilizes a deep reinforcement learning approach, inspired by diffusion models.
  • It decomposes the I2IT process into iterative steps using a lightweight model for progressive image transformation.
  • A meta policy with a low-dimensional 'concept Plan' is introduced to the Actor-Critic model to handle high-dimensional action spaces effectively.
  • A task-specific auxiliary learning strategy is employed to stabilize training and enhance performance.

Main Results:

  • Experiments on various I2IT tasks demonstrate the effectiveness and robustness of the RL-I2IT framework.
  • The method shows significant advantages in handling high-dimensional continuous action space problems.
  • The iterative decomposition approach leads to a more computationally efficient and less overfitted solution compared to single-step methods.

Conclusions:

  • The RL-I2IT framework offers a novel and efficient approach to Image-to-Image Translation by leveraging deep reinforcement learning.
  • The proposed method effectively addresses the limitations of traditional single-step DL models, particularly in managing complex, high-dimensional transformations.
  • The iterative, step-by-step transformation strategy provides a robust and performant solution for diverse I2IT tasks.