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Are Conditional Latent Diffusion Models Effective for Image Restoration?

Yunchen Yuan, Junyuan Xiao, Xinjie Li

TL;DR

The paper critically evaluates Conditional Latent Diffusion Models (CLDMs) for image restoration, arguing that despite their scalability, CLDMs struggle with preserving fine-grained details and semantic fidelity compared to traditional IR methods. By formalizing a CLDM-based IR pipeline with an initial restoration stage, perceptual latent compression, and conditioning, the authors show through extensive experiments that CLDMs often exhibit high distortion and semantic deviation, especially under mild degradation, while introducing an Alignment metric to capture semantic consistency. Ablation studies reveal that some architectural elements (e.g., multi-timestep sampling, latent-space transformations) do not improve restoration performance and can increase instability and latency, suggesting a misalignment between CLDM designs and IR tasks. The work concludes with a call for new evaluation frameworks and architectural adaptations to realize the potential of CLDMs in IR, and it provides a roadmap for future research in more faithful, alignment-aware restoration methods.

Abstract

Recent advancements in image restoration increasingly employ conditional latent diffusion models (CLDMs). While these models have demonstrated notable performance improvements in recent years, this work questions their suitability for IR tasks. CLDMs excel in capturing high-level semantic correlations, making them effective for tasks like text-to-image generation with spatial conditioning. However, in IR, where the goal is to enhance image perceptual quality, these models face difficulty of modeling the relationship between degraded images and ground truth images using a low-level representation. To support our claims, we compare state-of-the-art CLDMs with traditional image restoration models through extensive experiments. Results reveal that despite the scaling advantages of CLDMs, they suffer from high distortion and semantic deviation, especially in cases with minimal degradation, where traditional methods outperform them. Additionally, we perform empirical studies to examine the impact of various CLDM design elements on their restoration performance. We hope this finding inspires a reexamination of current CLDM-based IR solutions, opening up more opportunities in this field.

Are Conditional Latent Diffusion Models Effective for Image Restoration?

TL;DR

The paper critically evaluates Conditional Latent Diffusion Models (CLDMs) for image restoration, arguing that despite their scalability, CLDMs struggle with preserving fine-grained details and semantic fidelity compared to traditional IR methods. By formalizing a CLDM-based IR pipeline with an initial restoration stage, perceptual latent compression, and conditioning, the authors show through extensive experiments that CLDMs often exhibit high distortion and semantic deviation, especially under mild degradation, while introducing an Alignment metric to capture semantic consistency. Ablation studies reveal that some architectural elements (e.g., multi-timestep sampling, latent-space transformations) do not improve restoration performance and can increase instability and latency, suggesting a misalignment between CLDM designs and IR tasks. The work concludes with a call for new evaluation frameworks and architectural adaptations to realize the potential of CLDMs in IR, and it provides a roadmap for future research in more faithful, alignment-aware restoration methods.

Abstract

Recent advancements in image restoration increasingly employ conditional latent diffusion models (CLDMs). While these models have demonstrated notable performance improvements in recent years, this work questions their suitability for IR tasks. CLDMs excel in capturing high-level semantic correlations, making them effective for tasks like text-to-image generation with spatial conditioning. However, in IR, where the goal is to enhance image perceptual quality, these models face difficulty of modeling the relationship between degraded images and ground truth images using a low-level representation. To support our claims, we compare state-of-the-art CLDMs with traditional image restoration models through extensive experiments. Results reveal that despite the scaling advantages of CLDMs, they suffer from high distortion and semantic deviation, especially in cases with minimal degradation, where traditional methods outperform them. Additionally, we perform empirical studies to examine the impact of various CLDM design elements on their restoration performance. We hope this finding inspires a reexamination of current CLDM-based IR solutions, opening up more opportunities in this field.

Paper Structure

This paper contains 14 sections, 15 equations, 12 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Preliminary: Image restoration
  3. CLDM-based IR Pipeline
  4. Initial Restoration Module
  5. Perceptual Image Compression
  6. Conditioning Modules
  7. Training Process
  8. Sampling Process
  9. Issues in CLDM-based IR Solutions
  10. More Analysis on CLDMs
  11. Conclusion
  12. Overview
  13. More Analysis
  14. Future Work

Figures (12)

  • Figure 1: Perception-distortion tradeoff on CIR tasks.
  • Figure 2: Performance comparison on varying Gaussian blur levels.
  • Figure 3: Examples illustrating semantic deviation in CLDM outputs (DIFFBIR).
  • Figure 4: Comparison of performance relative to model parameters and latency.
  • Figure 5: Impact of latent space encoding on image details.
  • ...and 7 more figures