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Andrew Watson, Albert Ahumada; Blur clarified. Journal of Vision 2010;10(7):1385. doi: 10.1167/10.7.1385.
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© ARVO (1962-2015); The Authors (2016-present)
A review of the literature on blur detection and discrimination reveals a large collection of data, a few theoretical musings, but no predictive model. Among the key empirical findings are a “dipper” shaped function relating blur increment threshold to pedestal blur, as well as a nonlinear effect of luminance contrast. We have found that these phenomena and others are accounted for by a simple model in which discrimination is based on the energy of differences in visible contrast. Visible contrast is computed from the luminance waveform, as modified by local light adaptation and local contrast masking. The energy of the difference between two visible contrast waveforms, within a pooling aperture, determines threshold. This model can also predict detection thresholds for one dimensional waveforms such as Gabor signals. When fit to the ModelFest Gabors, it gives reasonable predictions for classic blur detection and discrimination data as well.
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