A conventional camera with small size pixels may capture images with defocused blurred regions. Blurring, as a low-pass filter, attenuates or drops details of the captured image. This fact makes deblurring as an ill-posed problem. Coded aperture photography can decrease
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A conventional camera with small size pixels may capture images with defocused blurred regions. Blurring, as a low-pass filter, attenuates or drops details of the captured image. This fact makes deblurring as an ill-posed problem. Coded aperture photography can decrease destructive effects of blurring in defocused images. Hence, in this case, aperture patterns are designed or evaluated based on the manner of reduction of these effects. In this paper, a new function is presented that is applied for evaluating the aperture patterns which are designed for defocus deblurring. The proposed function consists of a weighted sum of two new criteria, which are defined based on spectral characteristics of an aperture pattern. On the basis of these criteria, a pattern whose spectral properties are more similar to a flat all-pass filter is assessed as a better pattern. The weights of these criteria are determined by a learning approach. An aggregate image quality assessment measure, including an existing perceptual metric and an objective metric, is used for determining the weights. According to the proposed evaluation function, a genetic algorithm that converges to a near-optimal binary aperture pattern is developed. In consequence, an asymmetric and a semi-symmetric pattern are proposed. The resulting patterns are compared with the circular aperture and some other patterns in different scenarios.
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