DiffIR: Efficient Diffusion Model for Image Restoration (2303.09472v3)

Abstract: Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis process into a sequential application of a denoising network. However, different from image synthesis, image restoration (IR) has a strong constraint to generate results in accordance with ground-truth. Thus, for IR, traditional DMs running massive iterations on a large model to estimate whole images or feature maps is inefficient. To address this issue, we propose an efficient DM for IR (DiffIR), which consists of a compact IR prior extraction network (CPEN), dynamic IR transformer (DIRformer), and denoising network. Specifically, DiffIR has two training stages: pretraining and training DM. In pretraining, we input ground-truth images into CPEN${S1}$ to capture a compact IR prior representation (IPR) to guide DIRformer. In the second stage, we train the DM to directly estimate the same IRP as pretrained CPEN${S1}$ only using LQ images. We observe that since the IPR is only a compact vector, DiffIR can use fewer iterations than traditional DM to obtain accurate estimations and generate more stable and realistic results. Since the iterations are few, our DiffIR can adopt a joint optimization of CPEN$_{S2}$, DIRformer, and denoising network, which can further reduce the estimation error influence. We conduct extensive experiments on several IR tasks and achieve SOTA performance while consuming less computational costs. Code is available at \url{https://github.com/Zj-BinXia/DiffIR}.

• The paper introduces DiffIR, a diffusion model optimized for image restoration that reduces iteration count from thousands to only a few using a compact IPR.
• The methodology utilizes a two-stage training process with a CPEN for IPR extraction and a dynamic transformer (DIRformer) for efficient feature enhancement.
• Extensive experiments demonstrate DiffIR achieves up to 1000x efficiency gains over existing methods, establishing new benchmarks for image restoration performance.

Analyzing DiffIR: Efficient Diffusion Model for Image Restoration

The paper "DiffIR: Efficient Diffusion Model for Image Restoration" addresses the inefficiencies of traditional diffusion models (DMs) when applied to image restoration (IR) tasks. Image restoration, distinct from image synthesis, benefits from inherent low-quality (LQ) image references, obviating the need for the computationally intensive iterations of traditional DMs. The authors propose DiffIR, a diffusion model optimized specifically for IR, aiming to leverage DM's mapping capabilities while minimizing computational demands.

Methodology Overview

DiffIR is composed of three primary components: a Compact IR Prior Extraction Network (CPEN), a Dynamic IR Transformer (DIRformer), and a denoising network. The architecture and approach differ fundamentally from standard DMs, particularly in how they adapt to the constraints of IR.

• Training Stages: DiffIR is trained in two distinct stages. The first stage focuses on pretraining with ground-truth images to extract a Compact IR Prior Representation (IPR) that guides the DIRformer. The second stage tunes the DM to accurately predict the IPR using only LQ images, reducing iterations significantly compared to conventional methods.
• Dynamic Components: The DIRformer integrates dynamic transformer blocks that exploit IPR for feature extraction and enhancement. This dynamic modeling is achieved through mechanisms like Dynamic Gated Feed-Forward Network (DGFN) and Dynamic Multi-Head Transposed Attention (DMTA), emphasizing long-range and local dependencies in image data.
• Efficiency: DiffIR's innovative use of compact IPR allows it to perform with fewer iterations (e.g., four instead of thousands), significantly decreasing computational requirements while achieving state-of-the-art (SOTA) performance. The compact nature of IPR permits joint optimization of CPEN$_{S2}$, DIRformer, and the denoising network, further reducing the impact of estimation errors.

Results and Implications

Extensive experiments indicate that DiffIR offers superior performance across several IR tasks such as inpainting, super-resolution (SR), and deblurring. Notably, DiffIR achieves a 1000x efficiency improvement over RePaint on inpainting tasks, with marked improvements in computational overheads and performance measures like FID and LPIPS scores.

The results on real-world SR benchmarks further demonstrate DiffIR's capability to outperform existing methods by utilizing only a fraction of the computational resources. Moreover, sensitivity analyses and ablation studies suggest that specific design choices—such as not inserting variance noise in DM—are critical for optimal performance.

Theoretical and Practical Implications

Theoretical advancements in DiffIR lie in its refined approach to IR using a compact IPR, illustrating how targeted architectural changes can significantly enhance the applicability of DMs to IR. Practically, DiffIR offers a scalable solution for reducing resource consumption in environments where computational efficiency is paramount, such as mobile devices and real-time image processing systems.

Future Directions

Looking ahead, future developments could see DiffIR's methodology applied to other image-related tasks, potentially extending to video restoration or real-time applications. Additionally, further refinement of joint optimization strategies and transformer-based components could lead to additional performance gains.

In conclusion, DiffIR signifies a substantive step forward in the application of diffusion models to image restoration. By addressing the specific requirements and constraints of IR, it sets a benchmark for future research aimed at integrating sophisticated probabilistic models into practical, resource-efficient solutions.