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Time-Frequency Domain-Based No-Reference Algorithm for Image Blurriness Evaluation
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DOI：10.1007/978-981-95-3393-0_12.png)
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Abstract
En 中文
In high-definition imaging, solving the problem of out-of-focus blurring is one of the core challenges. For example, focus drift in telephoto lenses for distant targets affects subsequent analysis, necessitating a quantitative assessment of blur. Existing methods have drawbacks: subjective assessment lacks accuracy; reference-based objective methods rely on original, clear images, often unavailable in practice; feature-based no-reference methods have limitations and may misjudge complex images. For instance, EMBM is less sensitive to weak edges. Thus, a time-frequency domain no-reference assessment algorithm is proposed, with core innovations: first, a multi-scale feature extraction model integrating time-domain and frequency-domain features to comprehensively capture edge information across dimensions; second, a principal component analysis feature optimization module for dimensionality reduction and redundancy removal, enhancing key feature representation; finally, a dynamic weight allocation mechanism that specifically increases weak edge feature weights, solving EMBM weak edge neglect. Tests on the TID2013 dataset show that its SROCC index is 1.39% and 2.44% higher than that of EMBM, respectively.
Keywords:
Out-of-focus blur image
Time-frequency information fusion
Principal component analysis
No-reference evaluation algorithm
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