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.

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.

In this paper, ultrasonic images are initially deblurred using Gradient method and then the estimations of image and point spread function (PSF) are improved using denoising techniques. For this reason, at first a criterion with appropriate regularizers (that results in preservation of the edges) is defined for the iterative Gradient method, then the estimation of PSF is improved using a denoising technique based on using an anisotropic window around each pixel. The initial estimation of image is also improved using a denoising method in complex wavelet domain that proposes maximum a posteriori (MAP) estimator and local Laplacian prior density function. Using these denoising methods on top of Gradient method causes that our algorithm reduces the visual artifacts and preserves the edges in the deblurred images. Our simulations show that the proposed method in this paper outperforms other methods visually and quantitatively.

Magnetic Resonance Imaging (MRI) provides a non-invasive manner to aid clinical diagnosis, while its limitation is the slow scanning speed. Recently, due to the high costs of health care and taking account of patient comfort, some methods such as Parallel MRI (pMRI) and compressed sensing MRI have been developed to reduce the MR scanning duration under the sampling process. It is almost unavoidable to accept some doses of X-rays in computed tomography (CT scans). If one could find a more efficient way to represent the required visual information, the tasks of image processing and medical imaging would become easier and less troublesome. In this paper, first, we used pMRI on complex double data of brain magnetic resonance image. pMRI significantly reduces the number of measurements in the Fourier domain because each coil only acquires a small fraction of the whole measurements. It is important to reconstruct the original MR image efficiently and precisely for better diagnosis. In this research, we proposed a new super resolution and Deblurring algorithm with dictionary learning, based on assuming a local SparseLand model on image patches, serving as regularization, then we validated the proposed method by using another one called the adaptive selection of sub dictionaries-adaptive reweighted sparsity regularization. Visual comparison and significant difference in psnr calculation (0. 8111db) and time complexity showed that the proposed method had much better results.

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.