LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents (2412.04090v2)

Abstract: We present the first loss agent, dubbed LossAgent, for low-level image processing tasks, e.g., image super-resolution and restoration, intending to achieve any customized optimization objectives of low-level image processing in different practical applications. Notably, not all optimization objectives, such as complex hand-crafted perceptual metrics, text description, and intricate human feedback, can be instantiated with existing low-level losses, e.g., MSE loss, which presents a crucial challenge in optimizing image processing networks in an end-to-end manner. To eliminate this, our LossAgent introduces the powerful LLM as the loss agent, where the rich textual understanding of prior knowledge empowers the loss agent with the potential to understand complex optimization objectives, trajectory, and state feedback from external environments in the optimization process of the low-level image processing networks. In particular, we establish the loss repository by incorporating existing loss functions that support the end-to-end optimization for low-level image processing. Then, we design the optimization-oriented prompt engineering for the loss agent to actively and intelligently decide the compositional weights for each loss in the repository at each optimization interaction, thereby achieving the required optimization trajectory for any customized optimization objectives. Extensive experiments on three typical low-level image processing tasks and multiple optimization objectives have shown the effectiveness and applicability of our proposed LossAgent.

• The paper introduces LossAgent, the first LLM-based loss agent that dynamically adjusts loss weights for diverse, non-differentiable optimization objectives.
• It develops a compositional loss repository and employs prompt engineering to guide the LLM in optimizing various image processing tasks.
• Extensive experiments demonstrate significant improvements in NIQE and balanced gains in PSNR and perceptual quality across multiple image restoration benchmarks.

Overview of "LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents"

The paper introduces an innovative approach utilizing LLMs to enhance low-level image processing tasks, specifically through a mechanism termed as LossAgent. The authors aim to address the limitation of current loss functions that are often singularly oriented towards specific objectives, such as MSE, L1, or GAN-based approaches. These constraints become problematic in complex, real-world scenarios where multifaceted optimization objectives are necessary.

Main Contributions

1. Introduction of LossAgent: The paper proposes the first LLM-based loss agent, named LossAgent, which leverages LLaMA-3 as a pre-trained LLM to guide the optimization of image processing networks. This LLM acts as a dynamic loss function modifier that can intelligently adjust the compositional weights of existing loss functions to align with a variety of complex, non-differentiable optimization objectives like perceptual metrics or textual feedback from humans.
2. Compositional Loss Repository: The authors construct a repository of loss functions that support end-to-end optimization. The LossAgent uses this repository to adaptively and actively assign weights to these loss functions, effectively tailoring the optimization trajectory to suit custom requirements.
3. Prompt Engineering for Optimization: The paper details an optimization-oriented prompt engineering approach, which helps LossAgent to understand and respond accurately to the optimization states and trajectory. In addition to historical optimization data, this method uses system prompts and customized needs prompts to guide the decision-making process of the LLM.
4. Empirical Demonstration: LossAgent's performance is demonstrated through extensive experiments on various low-level image processing tasks, including classical and real-world image super-resolution and all-in-one image restoration. The results show the agent's capacity to handle diverse optimization objectives, confirming its flexibility and applicability.

Numerical Results and Claims

The experiments provide robust numerical results showcasing the efficacy of LossAgent. For instance, on classical image super-resolution tasks, LossAgent achieves a NIQE score improvement from the baseline by a significant margin across multiple datasets. This indicates that the LLM-based approach can effectively adjust loss weights to improve the perceptual quality of images. Furthermore, when competing objectives such as both Q-Align and PSNR are employed, the LossAgent is able to strike a balance that achieves substantial gains in PSNR without sacrificing Q-Align performance.

Implications

Theoretical Implications

This research introduces a new paradigm in image processing optimization by incorporating LLMs, traditionally applied within language tasks, into this domain. It suggests a general framework for leveraging the reasoning capabilities of LLMs to address challenges arising from non-differentiable optimization objectives. This interdisciplinary approach could prompt further exploration into the applicability of LLMs across various tasks requiring dynamic adaptability.

Practical Implications

Practically, LossAgent may provide a viable solution for real-world applications that require a balance of multiple complex requirements, such as improving visual quality according to human feedback or advanced, non-differentiable IQA metrics. Its flexibility in handling diverse optimization objectives could potentially lead to more efficient and tailored image processing solutions.

Future Directions

The development of LossAgent opens multiple avenues for future work. Researchers might explore the integration of other LLM architectures or tune the prompt engineering process for even greater adaptability and precision in various application domains. Further experimentation with even more complex optimization scenarios, including those integrating multimodal inputs and outputs, could also be a valuable extension to this work.

In conclusion, this paper presents a compelling case for utilizing LLMs as adaptive agents in image processing, offering a novel solution to an area traditionally dominated by fixed, singular optimization objectives. The results indicate promising directions for future research, potentially transforming approaches to image and multimedia processing.