Adaptive sharpening of photos

Ilia V. Safonov; Michael N. Rychagov; KiMin Kang; Sang Ho Kim

doi:10.1117/12.758613

28 January 2008 Adaptive sharpening of photos

Ilia V. Safonov, Michael N. Rychagov, KiMin Kang, Sang Ho Kim

Proceedings Volume 6807, Color Imaging XIII: Processing, Hardcopy, and Applications; 68070U (2008) https://doi.org/10.1117/12.758613
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

Sharpness is an important attribute that contributes to the overall impression of printed photo quality. Often it is impossible to estimate sharpness prior to printing. Sometimes it is a complex task for a consumer to obtain accurate sharpening results by editing a photo on a computer. The novel method of adaptive sharpening aimed for photo printers is proposed. Our approach includes 3 key techniques: sharpness level estimation, local tone mapping and boosting of local contrast. Non-reference automatic sharpness level estimation is based on analysis of variations of edges histograms, where edges are produced by high-pass filters with various kernel sizes, array of integrals of logarithm of edges histograms characterizes photo sharpness, machine learning is applied to choose optimal parameters for given printing size and resolution. Local tone mapping with ordering is applied to decrease edge transition slope length without noticeable artifacts and with some noise suppression. Unsharp mask via bilateral filter is applied for boosting of local contrast. This stage does not produce strong halo artifact which is typical for the traditional unsharp mask filter. The quality of proposed approach is evaluated by surveying observer's opinions. According to obtained replies the proposed method enhances the majority of photos.

Citation Download Citation

Ilia V. Safonov, Michael N. Rychagov, KiMin Kang, and Sang Ho Kim "Adaptive sharpening of photos", Proc. SPIE 6807, Color Imaging XIII: Processing, Hardcopy, and Applications, 68070U (28 January 2008); https://doi.org/10.1117/12.758613

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