Un-sharp masking method improves the images contrast without requiring any prior knowledge. In this method, a sharper image can be achieved by empowering the high frequency components of the input image. Un-sharp masking has a parameter named gain factor which has a high effect on the enhanced image quality. In this paper, an approach is proposed to adaptively estimate the appropriate value of this parameter in order to effectively enhance an image with local BLUR, or an image with non-uniform BLUR. In proposed method, first, the input image is segmented into BLUR and non-BLUR regions. Then the gain factor is estimated for each region adaptively. In this approach, the influence of the image BLURriness on its gradient information is used to estimate the value for the gain factor. The image quality assessments are applied to evaluate the performance of proposed un-sharp masking method in image enhancement. Experimental results demonstrate that the performance of our proposed method is better than the performance of existing un-sharp masking methods in image enhancement.

Super-resolution (SR) is a technique that produces a high resolution (HR) image via employing a number of low resolution (LR) images from the same scene. One of the degradations that attenuates performance of the SR is the BLURriness of the input LR images. In many previous works in the SR, the BLURriness of the LR images is assumed to be due to the integral effect of the image sensor of the acquisition device. However, in practice there are some other factors that BLUR the LR images, such as diffraction, motion of the object and/or acquisition device, atmospheric BLURring and defocus BLURring. To apply the super-resolution process accurately, the degradation model applied to HR image leading to LR ones needs to be known. In this paper, we aim to use the LR images BLURriness to find the BLURring kernel applied on the HR image. Hence, we setup a simulation experiment in which the BLURring kernel is limited to be one of the predetermined kernels. In the experiment, the BLURriness of the LR images is supposed to be unknown, and is estimated using a BLUR kernel estimation method. Then, the estimated BLUR kernels of the LR images are fed to an artificial neural network (ANN) to determine the BLUR kernels associated with the HR image. Experiment results show the use of determined BLUR kernels improves the quality of output HR image.

Technical limitations in image capturing usually impose defect, such as contrast degradation. There are different approaches to improve the contrast of an image. Among the exiting approaches, un-sharp masking is a popular method due to its simplicity in implementation and computation. There is an important parameter in un-sharp masking, named gain factor, which affects the quality of the enhanced image. In this paper, a new adaptive un-sharp masking method is proposed. In the proposed method gradient variation of the image is used to estimate the gain factor for un-sharp masking. Gradient variation of an image can provide information about the image contrast. Subjective and objective image quality assessments are used to compare the performance of the proposed method both with the classic and the recently developed un-sharp masking methods. The experimental results show the superiority of the proposed method compared to the existing methods in image enhancing using un-sharp masking.

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.

Classical image deconvolution seeks an estimate of the true image when the BLUR kernel or the point spread function (PSF) of the BLURring system is known a priori. However, blind image deconvolution addresses the much more complicated, but realistic problem where the PSF is unknown. Bayesian inference approach with appropriate priors on the image and the BLUR has been used successfully to solve this blind problem, in particular with a Gaussian prior and a joint maximum a posteriori (JMAP) estimation. However, this technique is unstable and suffers from significant ringing artifacts in various applications. To overcome these limitations, we propose a regularized version using $H^1$ regularization terms on both the sharp image and the BLUR kernel. We present also useful techniques for estimating the smoothing parameters.  We were able to derive an efficient algorithm that produces high quality deBLURred results compared to some well-known methods in the literature.

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.