# Methods for Improving Image Quality for Contour and Textures Analysis Using New Wavelet Methods
## 1 Introduction  
[PERSON]'s theory of optimal filtering of continuous signals is the basis of linear least squares linear error filters dependent on input data. [PERSON] studied in his 1949 paper \"Extrapolation, Interpolation and Smoothing of Stationary Time Series\", considering stationary signals, the problem of estimation in terms of the least squares error in the continuous case of time series. The extension of [PERSON]'s theory to the discrete case is simple and has a great practical utility because it led to the implementation of this type of filter using digital signal processors or specialized circuits in this class. Wiener filters play an important role in a wide range of applications [1] such as linear prediction, signal encoding, echo cancellation, signal recovery, channel equalization, signal identification or additive noise suppression, a number of studies focusing on their use in practical applications. The coefficients of the Wiener filter are calculated so as to minimize the average square error between the filter output and the useful signal. [PERSON], [PERSON] and [PERSON] [2] are researching with the purpose of improving the late reflection, noise power and the speech from a contaminated signal and improve it with a novelscheme. The solutions include an acoustic model (AM) and proves its robustness and effectiveness through experimental evaluation. The system proposed by [PERSON], [PERSON] and [PERSON] [3] involves a WaveShrink module and proposed an improved system of wavelet-based enhancement with a thresholding algorithm and a novel method for the selection of the threshold. The results are presenting, comparatively with similar approaches, an improvement in the system performances. [PERSON] [4] presents a new approach with an improved minima controlled recursive averaging (IMCRA) for noise estimation, low input signal to noise ratio (SNR) weak speech components. The IMCRA method proved to be effective and it has a lower estimation error compared with other methods, provides a lower residual noise and better speech quality. [PERSON] [5] is using a stand-alone noise suspension algorithm to reduce spectral effects of added noise. This system can be used for speech recognition systems, speaker authentication systems or narrow band voice as a pre-processor. [PERSON], [PERSON] and [PERSON] [6] are using Wiener filter to identify and estimate the mel frequency axis Mel-LPC spectra in a medium with additive noises. After the filter is applied, the transform of inverse wavelet is applied and it obtains the signal of enhanced time domain speech. The result has presented an improvement on the word accuracy by 17.22%. [PERSON] [7] uses a pyramidal algorithm with quadrature mirror filters. The representation presents several spatial orientations. This representation of the application may be used in fractal analysis, texture discrimination and image coding. [PERSON], [PERSON] and [PERSON] [8] want to apply first the maximum likelihood (ML) rule and then the estimation procedure MMSE--minimum mean squared error. Comparative with other results from literature, this method presents favorable result. [PERSON] and [PERSON] [9] research on thresholding and minimax/global wavelet shrinkage methods has proven to be very good in different scenarios. They are introducing a new group of priors with the base on Rissanen universal prior on integers. The theoretical and experimental results are presenting the robustness of some wrong specifications of others on image processing. [PERSON] and [PERSON] [10] are analyzing the noise removing problem starting from its solution, the Wiener solution and develop its extension, a Bayesian estimator. This estimator is nonlinear and performs a \"coring\" operation. Based on steerable wavelet pyramid, they create for subband statistics a simple model semiblind noise-removal algorithm. [PERSON] and [PERSON] [11] proposed for the modeling of natural images the Gaussian scale mixture. Using multiscale bases are two surprising types of non-Gaussian behavior.