Image enhancement improves the visual looks of the images . In this paper various algorithm which are used to enhance the quality of the image are surveyed ,special attention is given to L0 norm and ant colony based optimisation algorithms.

Natural-gradient (NG) adaptive algorithms are known to be superior to stochasticgradient (SG) algorithms when the channel to be identified exhibits a known Riemannian structure. In sparse channel identification, for example, the improved-proportionate normalized least-mean-square (IPNLMS) is a well known NG algorithm which outperforms the classical SG normalized least-mean square (NLMS). Apart ...

The problem of 1-bit compressive sampling is addressed in this paper. We introduce an optimization model for reconstruction of sparse signals from 1-bit measurements. The model targets a solution that has the least l0-norm among all signals satisfying consistency constraints stemming from the 1-bit measurements. An algorithm for solving the model is developed. Convergence analysis of the algori...

It is known that the sparseness of the factor matrices by Nonnegative Matrix Factorization can influence the clustering performance. In order to improve the ability of the sparse representations of the NMF, we proposed the new algorithm for Nonnegatie Matrix Factorization, coined nonnegative matrix factorization on orthogonal subspace with smoothed L0 norm constrained, in which the generation o...

This paper investigates the problem of image reconstruction of compressed sensing. First, an improved smoothed l0 norm (ISL0) algorithm is proposed by using modified Newton method to improve the convergence speed and accuracy of classical smoothed l0 norm (SL0) algorithm, and to increase calculation speed and efficiency. The choice of algorithm parameter is discussed and the algorithm convergen...

Edge-preserving image smoothing is one of the fundamental tasks in the field of computer graphics and computer vision. Recently, L0 gradient minimization (LGM) has been proposed for this purpose. In contrast to the total variation (TV) model which employs the L1 norm of the image gradient, the LGM model adopts the L0 norm and yields much better results for the piecewise constant image. However,...

Sparse coding algorithms are about finding a linear basis in which signals can be represented by a small number of active (non-zero) coefficients. Such coding has many applications in science and engineering and is believed to play an important role in neural information processing. However, due to the computational complexity of the task, only approximate solutions provide the required efficie...

In order to improve the sparsity exploitation performance of norm constraint least mean square (LMS) algorithms, a novel adaptive algorithm is proposed by introducing a variable p-norm-like constraint into the cost function of the LMS algorithm, which exerts a zero attraction to the weight updating iterations. The parameter p of the p-norm-like constraint is adjusted iteratively along the negat...

In blind motion deblurring, leading methods today tend towards highly non-convex approximations of the l0-norm, especially in the image regularization term. In this paper, we propose a simple, effective and fast approach for the estimation of the motion blur-kernel, through a bi-l0-l2-norm regularization imposed on both the intermediate sharp image and the blur-kernel. Compared with existing me...

In this paper, we investigate conditions for the unique recoverability of sparse integer-valued signals from few linear measurements. Both the objective of minimizing the number of nonzero components, the so-called l0-norm, as well as its popular substitute, the l1-norm, are covered. Furthermore, integer constraints and possible bounds on the variables are investigated. Our results show that th...