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Face stimuli were cropped to a round shape, blurred (radius 15 Adobe Photoshop [CS4] blur tool) and hair was removed.
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Relative fluorescence intensities were calculated by dividing the raw images with their blurred versions (Gaussian blur, radius 4 µm).
When the edge was blurred with an unknown Gaussian blur radius σ, the blurred edge was modeled as follows: b ( x ) = A 2 ( 1 + ∑ n = - x x g ( n, σ ) ) + B, x ≥ 0 A 2 ( 1 - ∑ n = x + 1 - x - 1 g ( n, σ ) ) + B, x < 0 (11).
To estimate the unknown blur radius σ, two re-blurred edges (b a (x), b b (x)) were obtained with the blur radii (σ a and σ b (σ a < σ b )).
The images were first contrast-enhanced by applying a Gaussian blur (radius = 2 pixel), subtracting the blurred sequence from the original image sequence, then adding the difference back to the original image sequence.
The proposed blur metric was computed by estimating the blur radius on the edge regions.
Then, we computed the blur radius only for the edge blocks.
Also, a blur radius estimated using a Gaussian blur kernel has been proposed to measure blurring artifacts [14, 15].
The proposed NR blur metric was motivated by the Gaussian blur radius estimator in [15], which was used for estimating an unknown Gaussian blur radius using two re-blurred images of the entire image.
where A and B are the constant values, and they do not influence the blur radius estimation.
where σ i represents the blur radius of the ith block and N B represents the total number of edge blocks.
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