提出新型锐度感知图像质量评估指标，提升去模糊效果真实感。
A Subjective Study on a New Sharpness Informed Class of Metrics
- 设计四阶段主观实验，量化人眼对图像锐度的偏好
- 新指标在基准数据集上相关性优于所有PSNR变体
- 含锐度感知损失的模型在67%对比中更受青睐
深度神经网络去模糊架构中的感知损失能提升恢复图像的整体质量，但很少有工作明确针对锐度进行优化。我们通过四阶段主观实验，研究了使用与不使用显式锐度目标损失训练的模型在图像质量上的差异，探索了理想的锐度水平及其影响。为此，我们构建了一个具有均匀锐度增量的全新数据集，并引入差分均值意见分数（DMOS）。同时，提出一类新的锐度感知（SI）图像质量评估（IQA）指标，能有效惩罚过度锐化。新提出的SI-PSNR指标在多个IQA基准数据集上的相关性统计表现超越所有其他PSNR变体。结果显示，平均而言，使用锐度感知复合损失恢复的图像在二值化比较中被偏好达67%，显著高于未显式关注锐度的损失方法。
原文摘要 · Abstract (English)
Perceptual loss functions in Deep Neural Network (DNN) deblurring architectures improve the overall quality of restored images. However, few focus on explicitly targeting sharpness in the restorations. We conduct a subjective study of models trained with and without losses which explicitly target sharpness using a four-protocol approach, exploring preferred sharpness levels and effects on image quality. We introduce a novel dataset of images with uniform sharpness increments along with Difference Mean Opinion Scores (DMOS). Additionally, we propose a novel class of Sharpness Informed (SI) Image Quality Assessment (IQA) metrics which properly penalize over-sharpening. Our new SI-PSNR metric outperforms all other PSNR variants in terms of correlation statistics on IQA benchmarking datasets. We show that, on average, images restored using a sharpness-aware composite loss are preferred in 67% of binarized comparisons, as opposed to losses that do not explicitly target sharpness.
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