We have implemented super-resolution techniques such as machine learning methods and solving a least squares problem with a prior to perform demosaicing. These techniques include k-nearest neighbors (KNN), linear regression, and alternating direction method of multipliers with a total variation prior (ADMM TV). For all methods, parameters were optimized to minimize the mean-squared-error (MSE) ...

We consider the problem of sequentially choosing between a set of unbiased Monte Carlo estimators to minimize the mean-squared-error (MSE) of a final combined estimate. By reducing this task to a stochastic multi-armed bandit problem, we show that well developed allocation strategies can be used to achieve an MSE that approaches that of the best estimator chosen in retrospect. We then extend th...

z In this paper, the one-dimensional principal component lter banks (PCFB's) derived in 17] are generalized to higher dimensions. As presented in 17], PCFB's minimize the mean-squared error (MSE) when only Q out of P subbands are retained. Previously, 2D PCFB's were proposed in 16]. The work in 16] was limited to 2D signals and separable resampling operators. The formulation presented here is g...

In this correspondence, a new time delay estimation procedure is proposed using the multiresolution analysis framework through a discrete wavelet transform (DWT). Once the signals are decomposed, the time delays are estimated iteratively in each sub-band using two different adaptation mechanisms that minimize the mean squared error (MSE) between the reference and primary signals in the correspo...

This paper studies three related algorithms: the (traditional) Gradient Descent (GD) Algorithm, the Exponentiated Gradient Algorithm with Positive and Negative weights (EG algorithm) and the Exponentiated Gradient Algorithm with Unnormalized Positive and Negative weights (EGU algorithm). These algorithms have been previously analyzed using the “mistake-bound framework” in the computational lear...

We consider model-based prediction of a finite population total when a monotone transformation of the survey variable makes it appropriate to assume additive, homoscedastic errors. As the transformation to achieve this does not necessarily simultaneously produce an easily parameterized mean function, we assume only that the mean is a smooth function of the auxiliary variable and estimate it non...

A frame design technique for use with vector selection algorithms, for example Matching Pursuits (MP), is presented. The design algorithm is iterative and requires a training set of signal vectors. The algorithm, called Method of Optimal Directions (MOD), is an improvement of the algorithm presented in [1]. The MOD is applied to speech and electrocardiogram (ECG) signals, and the designed frame...

It is well-known that the mean squared error (MSE) is an inappropriate measure for the difference between two images in many applications. For one such an application, edge-preserving smoothing, an alternative was developed which takes both goals into account: the preservation or sharpening of edges and the smoothing of regions. In this paper, tests on human subjects are reported which con rm t...

In multiple regression analysis, the independent variables should be uncorrelated within each other. If they are highly intercorrelated, this serious problem is called multicollinearity. There are several methods to get rid of this problem and one of the most famous one is the ridge regression. In this paper, we will propose some modified ridge parameters. We will compare our estimators with so...

This paper evaluates the usage of analytical programming and different fitness functions for software effort estimation. Analytical programming and differential evolution generate regression functions. These functions are evaluated by the fitness function which is part of differential evolution. The differential evolution requires a proper fitness function for effective optimization. The proble...