Abstract
We present two new metrics for measuring sharpness of an image. Both methods exploit a reorganized Discrete Cosine Transform (DCT) representation and analyze the reorganized coefficients to use the most useful components for sharpness measuring. Our first metric utilizes optimal high and middle frequency coefficients for relative sharpness evaluation. It is well suitable for focus measure as it is super sensitive to the best-focus position and could predict stable and accurate focus values for various subjects and scenes with different lighting and noise conditions. Experiments demonstrate that it has high discrimination power even for high noisy and low-contrast images. The second metric constructs energy maps for each scale of reorganized DCT coefficients, and determines absolute sharpness/blurriness using the local maxima energy information. Compared with most existing no-reference sharpness/blurriness metrics, this metric is very efficient in sharpness measurement for images with different contents, and can be used in real-time auto-focus application. Experiments show that it correlates well with perceived sharpness.
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Zhang, Z., Liu, Y., Tan, X., Zhang, M. (2015). Robust Sharpness Metrics Using Reorganized DCT Coefficients for Auto-Focus Application. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_12
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