Mohammad Aghagolzadeh

Learn More
Typical consumer digital cameras sense only one out of three color components per image pixel. The problem of demosaicing deals with interpolating those missing color components. In this paper, we present compressive demosaicing (CD), a framework for demosaicing natural images based on the theory of compressed sensing (CS). Given sensed samples of an image,(More)
This letter proposes a novel learning-based super-resolution method rooted in low dimensional manifold representations of high-resolution (HR) image-patch spaces. We exploit the input image and its different down-sampled scales to extract a set of training sample points using a min-max algorithm. A set of low dimensional tangent spaces is estimated from(More)
Partial Differential Equation (PDE) based diffusion has been utilized for image denoising for more than two decades. It is known that the process of diffusion preserves the edges and object boundaries making it a suitable preprocessing step for edge detection. Synergetic to these efforts, in this work, we apply diffusion to network graphs leading to an(More)
A typical consumer digital camera uses a Color Filter Array (CFA) to sense only one color component per image pixel. The original three-color image is reconstructed by interpolating the missing color components. This interpolation process (known as demosaicing) corresponds to solving an under-determined system of linear equations. In this paper, we show(More)
Compressive imaging reconstructs the original signal by searching through the feasible space for the solution with maximum compactness under a known frame or dictionary. With the extent of available optimization tools, the recovery performance mainly relies on the power of dictionary to sparsely represent the data. Universal dictionaries can be trained from(More)
Transitivity in friendship graphs has been well known as a key property of social networks. In this paper, we extend the graph transitivity index by introducing a new characteristic quantity of graphs, namely the transitivity matrix. The transitivity matrix measures the microscopic impact that each link has on the global transitivity index of the graph. We(More)
This paper considers the problem of single image super-resolution (SR). Previous example-based SR approaches mainly focus on analyzing the co-occurrence property of low resolution (LR) and high resolution (HR) patches via dictionary learning. In this paper, we propose a novel approach based on local linear approximation of the HR patch space using a sparse(More)
The utility of Compressed Sensing (CS) for demosaicing of digital images have been explored by few recent efforts [1][2][3]. Most recently, a Compressive Demosaicing [3] framework, based on employing a random panchromatic Color Filter Array (CFA) at the sensing stage, has provided compelling CS-based demosaicing results by visually outperforming other(More)
The utility of Compressed Sensing (CS) for demosaicing of images captured using random panchromatic color filter arrays (CFA) has been investigated in [1]. Meanwhile, most camera manufacturers employ periodic CFAs such as the popular Bayer CFA. In this paper, we derive a CS-based solution to demosaicing images captured using the general class of periodic(More)
In this paper, we tackle real-time learning of a dictionary D from compressive measurements Y of an image X. Existing dictionary learning algorithms are inapplicable because compressive samples Y = ΦX are incomplete and can be arbitrary linear combinations of different pixels. Our strategy is to learn a dictionary of the form D = ΨΘ,(More)