Abstract
Sharpness is an important attribute that contributes to the overall impression of photo quality. It is a complex task for a consumer to obtain an appropriate outcome by editing a photo on a computer, because it is impossible to estimate sharpness prior to printing. Our approach includes three key techniques: blind sharpness level estimation, local tone mapping, and boosting of local contrast. The sharpness metrics is based on an analysis of variations of histograms produced by high-pass filters while increasing the convolution kernel size. An array of sums of logarithms of such histograms characterizes the photo’s blurriness. We use machine learning for the selection of parameters for a given printing size and resolution. Local tone mapping decreases the length of the edge transition slope. An unsharp mask implemented via a bilateral filter boosts the local contrast. The stage does not produce a strong halo artefact as is typical for a traditional unsharp mask filter. The quality of the proposed approach was assessed by a survey of observers. According to the replies obtained, the proposed method enhances the majority of photos from a test set.
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Safonov, I.V., Kurilin, I.V., Rychagov, M.N., Tolstaya, E.V. (2018). Adaptive Sharpening. In: Adaptive Image Processing Algorithms for Printing. Signals and Communication Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-6931-4_4
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DOI: https://doi.org/10.1007/978-981-10-6931-4_4
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