InstantIR: Blind Image Restoration with Instant Generative Reference

Abstract: Handling test-time unknown degradation is the major challenge in Blind Image Restoration (BIR), necessitating high model generalization. An effective strategy is to incorporate prior knowledge, either from human input or generative model. In this paper, we introduce Instant-reference Image Restoration (InstantIR), a novel diffusion-based BIR method which dynamically adjusts generation condition during inference. We first extract a compact representation of the input via a pre-trained vision encoder. At each generation step, this representation is used to decode current diffusion latent and instantiate it in the generative prior. The degraded image is then encoded with this reference, providing robust generation condition. We observe the variance of generative references fluctuate with degradation intensity, which we further leverage as an indicator for developing a sampling algorithm adaptive to input quality. Extensive experiments demonstrate InstantIR achieves state-of-the-art performance and offering outstanding visual quality. Through modulating generative references with textual description, InstantIR can restore extreme degradation and additionally feature creative restoration.

• The paper introduces INSTANT IR, a diffusion-based model that utilizes instant generative references to guide blind image restoration.
• It employs key modules like the Degradation Content Perceptor and Latent Aggregator to extract and merge semantic details from degraded images.
• Experimental results demonstrate state-of-the-art performance on non-reference metrics, emphasizing its potential for applications in autonomous driving and media content.

Analysis of "INSTANT IR: Blind Image Restoration with Instant Generative Reference"

The preprint "INSTANT IR: Blind Image Restoration with Instant Generative Reference" proposes a novel approach for Blind Image Restoration (BIR) using a diffusion-based model, named INSTANT IR. The method dynamically adjusts generation conditions during inference to tackle the ill-posed nature of restoring high-quality images from degraded inputs without prior knowledge of the degradation. Leveraging prior understanding from diffusion probabilistic models (DPM), this approach introduces an innovative pipeline that incorporates compact representation and instant generative references to guide and refine the restoration process.

Methodology and Architecture

INSTANT IR employs a pre-trained DPM to iteratively refine its restoration process. The architecture includes three pivotal modules:

1. Degradation Content Perceptor (DCP): This module uses a vision encoder to obtain a compact representation of low-quality (LQ) images, capturing high-level semantics and structural information. Its robustness against diverse degradation conditions is enhanced by a high compression rate, yet it retains crucial semantic content.
2. Instant Restoration Previewer: It creates generative references using distilled diffusion probabilistic models. These references are generated from current diffusion latent rather than noise at each restoration step, providing previews that guide the restoration process. The method ensures that generative references are informative by using a consistency distillation technique, thereby aligning them with the learned distribution and enhancing sampling efficiency.
3. Latent Aggregator: This module integrates generative references with the LQ input, preserving detail and preventing divergences during restoration. The Aggregator facilitates a robust combination of original and reference features using spatial attention and feature transform techniques.

The paper introduces an adaptive restoration mechanism using an algorithm (AdaRes), which adjusts the influence of generative references based on the input quality. This dynamic adjustment is grounded in data-driven metrics which reflect the input's informational richness.

Experimental Results

INSTANT IR claims state-of-the-art performance in blind image restoration. Extensive qualitative and quantitative evaluations are performed on synthetic and real-world datasets. Notably, while the system excels in non-reference metrics such as MUSIQ and MANIQA, it demonstrates some disparity in reference metrics such as PSNR and SSIM compared to other models, likely due to the generative components affecting fidelity. This finding is crucial as it suggests that the generative alignment might trade-off some degree of fidelity for enhanced semantic accuracy and detail richness.

Implications and Future Directions

The implications of INSTANT IR are manifold. Practically, it enhances the potential usability across industries reliant on image data, like autonomous driving and media content creation, enabling restoration from unknown degradation conditions with minimal dependence on additional reference inputs. Theoretically, it positions generative models and diffusion probabilistic frameworks as formidable tools in tackling BIR challenges, showcasing adaptability and creativity in restoration processes.

Future directions could investigate the interplay between generative prior and fidelity, potentially through novel ways to modulate and enhance the interaction between input conditions and generative references. Refinement of the previewer for more accurate and reliable references could also be beneficial, expanding the capability of interacting seamlessly with diverse image domains.

The paper marks an advancement in leveraging diffusion-based generative models for image restoration, striking a balance between detail enhancement and adherence to semantic integrity, though with some consideration needed for traditional fidelity metrics. As the field continues to evolve, further exploration of these generative paradigms may unlock new potentials in diverse vision applications.