用数学与心理学结合方法，找出让图像去噪更符合人眼感知的参数。
Mathematical framework for perception-driven parameter choice in image denoising
- 融合数学与心理测量学，构建感知相似性量化框架。
- 通过人眼对比实验，确定最优去噪参数阈值。
- 公开可复现的去噪图像数据集，适合感知研究者使用。
本文从感知驱动角度研究图像去噪：如何选择最符合人类视觉感知的参数？结合数学与心理学研究方法，提出一种测量感知相似性的数学框架。通过同一基础图像输入，调节总变差（Total Variation）去噪算法的参数，生成一系列不同去噪程度的照片样本。组织人类参与者进行对比测试，评估图像间的感知差异。利用心理测量标度分析结果，获得用于离散化参数网格的HaarPSI阈值。最终形成心理测量标度化的、公开可用的图像数据集，可直接用于后续感知驱动成像研究，同时提供一套可用于对比测试的实验框架。
原文摘要 · Abstract (English)
We approach image denoising from a perception-driven perspective: how can we select the parameters that are best suited for human visual perception? We combine research methods in mathematics and psychology to develop a mathematical framework for measuring perceived similarity. We construct a sample set of differently denoised photographs by using the same base image as input data and by tuning the parameter value in a total variation denoising algorithm. A comparison test is conducted with human participants to survey perceived differences between the images. Analyzing the results with psychometric scaling provides us with a HaarPSI value to use as a threshold in discretizing parameter grids. As a result, we obtain psychometrically scaled, openly available image sets that are ready to use in further experiments in perception-driven imaging, as well as a framework for ensuing experiments involving comparison tests.
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