Background and Objectives: depth from defocus and defocus deblurring from a single image are two challenging problems caused by the finite depth of field in conventional cameras. Coded aperture imaging is a branch of computational imaging, which is used to overcome these two problems. Up to now, different methods have been proposed for improving the results of either defocus deblurring or depth estimation. In this paper, an asymmetric coded aperture is proposed which improves results of depth estimation and defocus deblurring from a single input image.Methods: To this aim, a multi-objective optimization function taking into consideration both deblurring results and depth discrimination ability is proposed. Since aperture throughput affects on image quality, our optimization function is defined based on illumination conditions and camera specifications which yields an optimized throughput aperture. Because the designed pattern is asymmetric, defocused objects on two sides of the focal plane can be distinguished. depth estimation is performed using a new algorithm, which is based on perceptual image quality assessment criteria and can discern blurred objects lying in front or behind the focal plane.Results: Extensive simulations as well as experiments on a variety of real scenes are conducted to compare the performance of our aperture with previously proposed ones.Conclusion: Our aperture has been designed for indoor illumination settings. However, the proposed method can be utilized for designing and evaluating appropriate aperture patterns for different imaging conditions.

Super-resolution is a process that combines information from some low-resolution images in order to produce an image with higher resolution. In most of the previous related work, the blurriness that is associated with low resolution images is assumed to be due to the integral effect of the acquisition device’ s image sensor. However, in practice there are other sources of blurriness as well, including atmospheric and motion blur that may be applied to low resolution images. The research done in this paper provides a super-resolution image from some low-resolution images suffering from blurriness due to defocus. In contrast to motion blur kernels that are sparse, the defocus blur kernel is non-sparse and continuous. Because of the continuity property of defocus blurring kernel, in this paper, we bound the gradient of blurring kernel using proper regularizers to satisfy this property. Experimental results on synthetic data demonstrate the effectiveness of the proposed method to produce high resolution and de-blurred images from some blurry low-resolution images.

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.

Light is never being distributed homogeneously and it forms a gradient over biomass. The unidirectional nature of light gives rise to a vertical gradient of light intensity as a function of depth. The maximum depth of the light zone suitable for phytoplankton photosynthesis is designated as the euphotic depth. This study was designed to test the hypothesis of mixing depth of phytoplankton and its compensation depth. In a water column undergoing turbulent mixing, where phytoplankton cells are around by the fluid motion, the irradiance encountered by cells will be a function of the ratio between the euphotic depth (zy) and the depth of mixing (zm). During the course of study the zy/zm ratio value were observed in the range of 0.4427 to 3.242. However, identification of Compensation point values for zy/zm indicates that light conditions are suitable for net phytoplankton production, because the mean value (0.3413) was good for the phytoplanktonic growth, which was greater than the minimum 0.20 limit.