Feature Denoising Diffusion Model for Blind Image Quality Assessment
Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks. Currently, deep learning BIQA methods typically depend on using features from high-level tasks for transfer learning. However, the inherent differences between BIQA and these high-level tasks inevitably introduce noise into the quality-aware features. In this paper, we take an initial step towards exploring the diffusion model for feature denoising in BIQA, namely Perceptual Feature Diffusion for IQA (PFD-IQA), which aims to remove noise from quality-aware features. Specifically, (i) We propose a {Perceptual Prior Discovery and Aggregation module to establish two auxiliary tasks to discover potential low-level features in images that are used to aggregate perceptual text conditions for the diffusion model. (ii) We propose a Perceptual Prior-based Feature Refinement strategy, which matches noisy features to predefined denoising trajectories and then performs exact feature denoising based on text conditions. Extensive experiments on eight standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods, i.e., achieving the PLCC values of 0.935 ( vs. 0.905 in KADID) and 0.922 ( vs. 0.894 in LIVEC).
Code (0)
등록된 구현이 없습니다.
Tasks
Blind Image Quality AssessmentDenoisingImage Quality AssessmentTransfer LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild
Blind image quality assessment (IQA) in the wild, which assesses the quality of images with complex authentic distortions and no reference images, presents significant challenges. Given the difficulty in collecting large…
DenoisingImage Quality AssessmentNo-Reference Image Quality AssessmentLearning Generic Diffusion Processes for Image Restoration
Image restoration problems are typical ill-posed problems where the regularization term plays an important role. The regularization term learned via generative approaches is easy to transfer to various image restoration,…
DenoisingImage RestorationPlug-and-Play Posterior Sampling for Blind Inverse Problems
We introduce Blind Plug-and-Play Diffusion Models (Blind-PnPDM) as a novel framework for solving blind inverse problems where both the target image and the measurement operator are unknown. Unlike conventional methods th…
Blind Image DeblurringDeblurringDenoisingImage Deblurring+1Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising
In this work, we present Blind-Spot Guided Diffusion, a novel self-supervised framework for real-world image denoising. Our approach addresses two major challenges: the limitations of blind-spot networks (BSNs), which of…
Image DenoisingFrequency-Aware Guidance for Blind Image Restoration via Diffusion Models
Blind image restoration remains a significant challenge in low-level vision tasks. Recently, denoising diffusion models have shown remarkable performance in image synthesis. Guided diffusion models, leveraging the potent…
Blind Image DeblurringDeblurringDenoisingImage Deblurring+2