Let F be the original gray level image and G be the observed image degraded by additive independent identically distributed Gaussian noise of zero mean and standard deviation a.. Model b[r]

The nonlocal means (NLM) filtering scheme has gained an increasingly interest in the last decade for its great performance in **image** restoration. This scheme yields attractive results in removing **Gaussian** **noise** from an **image** by replacing the intensity value of each pixel by weighted average of the pixel intensities in a search neighborhood in the **image**. It is primarily based on repeated patterns that often exit in images. The selection of the kernel functions in such nonlocal means **image** restoration scheme is a major concern of researchers in order to improve the restored **image** quality. The **Gaussian** function is a standard kernel function commonly applied in the NLM filtering. In this paper, two functions for a nonlocal means **image** filtering scheme are proposed through a specific NLM method using an adaptive window size that varies according to the characteristics of the search regions in the **image**. **Zero**-**mean** **Gaussian** **noise** with different values of standard deviation corrupting various images of different characteristics has been used in the computer simulations. **Mean** squared error (MSE) and **mean** absolute error (MAE) have been used as measuring indices for the quality of the output restored **image**. Results show that these two functions work well and yield better performance mainly for images with a lot of details and edges than the conventional NLM scheme that uses the **Gaussian** kernel function and a fixed window size.

Mostrar más
Figure 3.14: Left: Result of adding zero mean, Gaussian white noise n with standard deviation $ before passing the scan lines of the image through a five level uniform quantizer, illustr[r]

In this, denoising plays a vital role. This is due to the emergence of noisy **image** sequences, such as those captured by cellular phones, recorded by webcams and old archive movies. In many cases, the **noise** is assumed to be **zero** **mean** **Gaussian** **noise**, is also common in the still **image** denoising literature.In the proposed method ,a qualitative **image** ehnacement approaches were used for improving the quality of the image.Figure 1 shows the block diagram of the enchancement technique carried in the proposed method for bio medical images.

Mostrar más
A signal or an **image** can be unfortunately corrupted by various factors which effects as **noise** during acquisition or transmission. The de-noising process is described as to remove the **noise** while retaining and not affecting the quality of processed signal or **image**. The conventional way of de-noising is to remove the **noise** from a signal or an **image** is to use a low or band pass filter with cut off frequencies. However the filtering techniques are able to remove only a relevant of the **noise**, they are incapable if the **noise** in the band of the signal is to be analyzed. Therefore, many de-noising techniques are proposed to overcome this problem. One of those is the wavelet transform (WT) processes.

Mostrar más
(Ansari & Mangla, 2015) had proposed a method for Eliminating **Noise** from Mixed Noisy **Image** by using Modified Bilateral Filter. To remove **noise** from images neighborhood processing is to be used. In Neighborhood processing, a function is applied to a neighborhood of each pixel. During this a rectangular mask (usually with sides of odd length) is moved over the given **image**. As we do this, we create a new **image** whose pixels have grey values calculated from the grey values under the mask. Mixed **noise** is considered as mixture of **Gaussian** **noise** and impulse **noise**. Some of the techniques are implemented in the work and analyzed in terms of various performance matrices. By understanding advantages, drawbacks and limitation of these techniques, we proposed an optimum technique for removing mixed **noise** from color **image**. Author proposed a SBF technique for this purpose.

Mostrar más
Consequently, the orthogonal approach is preferred for parameterized modeling. 3) The FRFs associated with the shaker input are severely distorted at frequencies higher than 100 Hz. Unlike the classical function in Fig. 3(a) and its coherence in Fig. 3(b) which indicate unreliable modelling quality at these frequencies, the source of distortions in the robust MIMO LPM method of (Pintelon, Vandersteen, et al. 2011) are associated with **noise**/nonlinear contributions. Physically, the nonlinear distortion is due to the nature of the connection between the shaker and the beam, i.e., the rubber- band (see Fig. 1). The reason for using a rubber-band instead of a direct connection (adhesive wax, screws, etc.) is to match the impedance of the (control) input signals realized by the piezo-patches with the (disturbance) signal generated by the electromagnetic shaker (Pintelon et al. 2012). 4) In the results of the orthogonal multisine method, the contribution of nonlinear distortions can be neglected since the total variance is 40 dB below the BLA. However, depending on the application, this may become non-negligible. Before proceeding to the assessment of input excitation optimization in MIMO smart structures, two remarks should be made regarding the importance of the MIMO robust LPM: a) The total variance not only reflects the quality of the estimated

Mostrar más
Wiener2 is a 2-D adaptive **noise** removal filter. The wiwner2 function applies a wiener filter which is a type of linear filter to an **image** adaptively, tailoring itself to local **image** variance. Where the variance is large, wiener2 performs little smoothing. Where the variance is small, wiener2 performs more smoothing. This approach often produces better result than linear filtering. The adaptive filter is more selective than a comparable linear filter, preserving edges and other high frequency parts of an **image**. In addition, there are no design tasks; the wiener2 function handles all preliminary computations, and implements the filter for preliminary computations, and implements the filter for an input **image**. Best suitable to remove **Gaussian** **noise**.

Mostrar más
2. **NOISE** MODEL:-**Noise** is the unwanted element produced in the **image**. During **image** acquisition or transmission, several factors are responsible for generating **noise** in the **image** [3]. Depending on the type of disturbance, the **noise** can affect the **image** to a different extent. Generally researchers aim to remove certain kind of **noise**. Therefore researchers identify certain kind of **noise** and apply different algorithms accordingly to eliminate the **noise**. **Image** **noise** can be categorised as Impulse **noise** (Salt-and-pepper **noise**), Amplifier **noise** (**Gaussian** **noise**), Shot **noise**, Multiplicative **noise** (Speckle **noise**), Quantization **noise** (uniform **noise**), and Periodic **noise** [3].

Mostrar más
The median is a more robust average than the **mean** and so a single very unrepresentative pixel in a neighbourhood will not affect the median value significantly. Since the median value must actually be the value of one of the pixels in the neighbourhood, the median filter does not create new unrealistic pixel values when the filter straddles an edge. For this reason the median filter is much better at preserving sharp edges than the **mean** filter.[5]

estimated. To ensure a good precondition for the subsequent **image** processing **noise**. For Gauss **noise**, new wavelet threshold function with the idea of limitation is applied, and the appropriate control variables m and are proposed to decrease the deviation between the wavelet coefficients and the original wavelet coefficients. The experiment results show that the **noise** mixed by both of the salt-and-pepper **noise** and the **Gaussian** **noise** is effectively suppressed. The **image** information is better preserved.

Mostrar más
Deconvolution is the process used to reverse the effects of convolution on image.The concept of deconvolution is popular used in the techniques of signal processing and **image** processing.It is the process of separation of blurred **image** from the original **image**. The objective is to find the original **image** from the observed blurred **image** which is degraded by an additive **Gaussian** **noise** and blur kernel.The **noise** from the given input **image** is found by using Fourier domain deconvolution.We can retain the spatial information of the **image** using Fourier domain.To remove the artifacts from the **image** without excessive slow down in computation,iterated shrinkage deconvolution technique used.With these results will be improved in both visual quality and **mean** square error. In fact, the wiener filter method is used for deconvolution.Its purpose to decrease the amount of blur available in the **image** by comparison with an estimation of the desired noiseless image.This technique gives proper solution for multiple images by removing artifacts without excessive slow down.This algorithm can computationally be improved by providing very low complexity. To apply the MATLAB edge taper function is to smooth the transition between the opposite sides of the images(part of the **Image** Processing Toolbox).

Mostrar más
The Non-Local Means algorithm searches neighboring patches to match with the reference patch. The original algorithm requires an extensive amount of time to select patches similar to the reference patch. These similar patches contribute to the weighted averaging process to denoise the center pixel of the reference patch. The computation time for NLM algorithm can be reduced by improving this searching process. In our method, we have created feature vectors for the noisy **image**. Then we have implemented a statistical t-test on these feature vectors and reduced their dimensionality. These reduced feature vectors contribute to the denoising process. Our proposed method reduces the computational time and improves the overall performance of the original NLM algorithm.

Mostrar más
In this section, experimental results are presented which explored the characteristics of the various filters used and tested. The comparative analysis has been presented on the basis of different standard deviation of **noise** for the original **image** (512*512) which is shown in the Table (1). The result is taken by comparing the performance of Fuzzy Filter and **Mean** Filter on the basis of PSNR and MSE value.

Abstract – DICOM has become a standard for medical imaging. Its purpose is to standardize digital medical imaging and data for easy access and sharing. There are many commercial viewers that support DICOM **image** format and can read metadata, but **image** displaying is not always optimal. One of the most common degradations in medical images is their poor contrast quality and **noise**. The DICOM **image** consists of speckle **noise**. While the **image** is enhanced, the different types of noises present in the **image** are also enhanced [5]. During our study we find there is problem in correct displaying for DICOM images in MATLAB. And in this paper we develop new method for correct displaying and de-noising of DICOM images concentrating on liver images.

Mostrar más
It is a better method because it extracts the features in an **image** without disturbing its features. There are certain criteria to improve current methods of edge detection. The first and most obvious is low error rate. It is important that edges occurring in images should not be missed. The second criterion is that the edge points be well localized i.e. the distance between the edge pixels as found by the detector and the actual edge should be minimum. A third criterion is to have only one response to a single edge [4].

results, and have become the most popular method in face recognition. But a real system may encounter with some difficulties which are unexpected, for example when the camera or the subject is moving, when the camera is defocus or when the **image** has been influenced by **noise**, we need methods to be robust to such problems.

Our simulation results show that each statistical measure has significant importance depending upon requirements. So selection of statistical measures is very important and should be done wisely. By using simulated results as reference it is very easy to select the statistical parameter before going for a complex **image** processing technique. Like from simulation results we concluded that arithmetic **mean**, geometric **mean**, harmonic **mean** all three can be used for removing **Gaussian** **noise** but geometric **mean** preserve the **image** details better. Contra-harmonic **mean** ﬁlter can be used to remove salt or pepper **noise** for different values of R. Median is commonly used for salt and pepper **noise**. Max and min ﬁlter is used for ﬁltering pepper and salt **noise** respectively. Mid-point can be used to reduce **Gaussian** and uniform **noise** in images. Alpha-trimmed ﬁlter can be used to remove more than once type of **noise**. Mode is basically used for **image** classification. Standard deviation based filter can used in radar for pattern recognition. Covariance and variance based filter can be used for edge sharpening, appearance based face detection and edge positioning. Skewness can be used for **image** surface recognition and kurtosis for **image** resolution.

Mostrar más