When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents

• URL: http://arxiv.org/abs/2403.06406v2
• Date: Wed, 15 Jan 2025 12:36:24 GMT
• Title: When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents
• Authors: Weixia Zhang, Dingquan Li, Guangtao Zhai, Xiaokang Yang, Kede Ma,
• Abstract summary: No-reference image quality assessment (NR-IQA) models can effectively quantify perceived image quality. We show that NR-IQA models can be plugged into the maximum a posteriori (MAP) estimation framework for image enhancement.
• Score: 92.45867913876691
• License:
• Abstract: Contemporary no-reference image quality assessment (NR-IQA) models can effectively quantify perceived image quality, often achieving strong correlations with human perceptual scores on standard IQA benchmarks. Yet, limited efforts have been devoted to treating NR-IQA models as natural image priors for real-world image enhancement, and consequently comparing them from a perceptual optimization standpoint. In this work, we show -- for the first time -- that NR-IQA models can be plugged into the maximum a posteriori (MAP) estimation framework for image enhancement. This is achieved by performing gradient ascent in the diffusion latent space rather than in the raw pixel domain, leveraging a pretrained differentiable and bijective diffusion process. Likely, different NR-IQA models lead to different enhanced outputs, which in turn provides a new computational means of comparing them. Unlike conventional correlation-based measures, our comparison method offers complementary insights into the respective strengths and weaknesses of the competing NR-IQA models in perceptual optimization scenarios. Additionally, we aim to improve the best-performing NR-IQA model in diffusion latent MAP estimation by incorporating the advantages of other top-performing methods. The resulting model delivers noticeably better results in enhancing real-world images afflicted by unknown and complex distortions, all preserving a high degree of image fidelity.

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