Ali Ayremlou

FlatCam: Thin, Lensless Cameras Using Coded Aperture and Computation

FlatCam is a thin form-factor lensless camera that consists of a coded mask placed on top of a bare, conventional sensor array. Unlike a traditional, lens-based camera, where an image of the scene is directly recorded on the sensor pixels, each pixel in FlatCam records a linear combination of light from multiple scene elements. A computational algorithm is then used to demultiplex the recorded measurements and reconstruct an image of the scene. FlatCam is an instance of a coded aperture imaging system; however, unlike the vast majority of related work, we place the coded mask extremely close to the image sensor that enables thin and flat form-factor imaging devices. We employ a separable mask to ensure that both calibration and image reconstruction are scalable in terms of memory requirements and computational complexity. We demonstrate the potential of the FlatCam design using two prototypes: one at visible wavelengths and one at infrared wavelengths.

Optimized Compact-Support Interpolation Kernels

In this paper, we investigate the problem of designing compact-support interpolation kernels for a given class of signals. By using calculus of variations, we simplify the optimization problem from an nonlinear infinite dimensional problem to a linear finite dimensional case, and then find the optimum compact-support function that best approximates a given filter in the least square sense (ℓ2 norm). The benefit of compact-support interpolants is the low computational complexity in the interpolation process while the optimum compact-support interpolant guarantees the highest achievable signal-to-noise ratio (SNR). Our simulation results confirm the superior performance of the proposed kernel compared to other conventional compact-support interpolants such as cubic spline.

FlatCam: Replacing Lenses with Masks and Computation

We present a thin form-factor lensless camera, FlatCam, that consists of a coded mask placed on top of a bare, conventional sensor array. FlatCam is an instance of a coded aperture imaging system in which each pixel records a linear combination of light from multiple scene elements. A computational algorithm is then used to demultiplex the recorded measurements and reconstruct an image of the scene. In contrast with vast majority of coded aperture systems, we place the coded mask extremely close to the image sensor that can enable a thin system. We use a separable mask to ensure that both calibration and image reconstruction are scalable in terms of memory requirements and computational complexity. We demonstrate the potential of our design using a prototype camera built using commercially available sensor and mask.

Improved iterative techniques to compensate for interpolation distortions

In this paper a novel hybrid algorithm for compensating the distortion of any interpolation has been proposed. In this Hybrid method, a modular approach was incorporated in an iterative fashion. The proposed technique features an impressive improvement at reduced computational complexity. The authors also extend the scheme to 2-D signals and actual (real) images and find that the solution exhibits great potential. Both the simulation results and mathematical analysis confirm the superiority of the Hybrid model over competing methods and demonstrate its robustness against additive noise.

Adaptive step size selection for optimization via the ski rental problem

Optimization has been used extensively throughout signal processing in applications including sensor networks and sparsity based compressive sensing. One of the key challenges when implementing iterative optimization algorithms is to choose an appropriate step size for fast algorithms. We pose the problem of choosing step sizes as solving a ski rental problem, a popular class of problems from the computer science literature. This results in a novel algorithm for adaptive step size selection that is agnostic to the choice of the optimization algorithm. Our numerical results show the advantages of using adaptivity for step size selection.

A novel method in adaptive image enlargement

This article introduces a new adaptive method for image interpolation. In order to obtain a high resolution (HR) image from its low resolution (LR) counterpart (original image), an interpolator function (array) is used, and the main focus of this manuscript is to formulate and define this function. By applying this interpolator function to each row and column of a LR image, it is possible to construct its HR counterpart. One of the main challenges of image interpolation algorithms is to maintain the edge structures while developing an HR image from the LR replica. The proposed approach overcomes this challenge and exhibits remarkable results at the image edges. The peak signal to noise ratio and structural similarity criteria by using this innovative technique are notably better than those achieved by alternative schemes. Also, in terms of implementation speed, this method displays a clear advantage and outperforms the high performance algorithms in the ability to decrease the artifact results of image enlargement such as blurring and zigzagging.

Compensating interpolation distortion by new optimized modular method

A modular method was suggested before to recover a band limited signal from the sample and hold and linearly interpolated (or, in general, an nth-order-hold) version of the regular samples. In this paper a novel approach for compensating the distortion of any interpolation based on modular method has been proposed. In this method the performance of the modular method is optimized by adding only some simply calculated coefficients. This approach causes drastic improvement in terms of signal-to-noise ratios with fewer modules compared to the classical modular method. Simulation results clearly confirm the improvement of the proposed method and also its superior robustness against additive noise.

Compensating for distortions in interpolation of two-dimensional signals using improved iterative techniques

In this paper we extended a previously investigated modular method that is designed to compensate for interpolation distortions of one-dimensional signals, to two dimensions (2-D). Next the proposed 2-D modular technique was applied in an iterative fashion and was shown through both simulations and theoretical analyses to enhance the convergence of the iterative technique. In fact, with only a few modules we were able to achieve drastic improvements in signal reconstruction, and with a much less computational complexity. Moreover, both the simulations and the theoretical analysis confirmed the robustness of the proposed scheme against additive noise.