Impact & Analysis of DVROFT Filter on TEM Image Garima Goyal Assistant Professor, Jyothy Institute of Technology Bangalore, India Abstract-TEM images are rapidly gaining prominence in various sectors like life sciences, pathology, medical science, semiconductors, forensics, etc. Hence, there is a critical need to know the effect of existing image restoration and enhancement techniques available for TEM images. This paper primarily focuses on DVROFT filter. After simulation it is observed that the SNR and PSNR ratios obtained for TEM image is much higher than those obtained for normal image. DVROFT give better performance than the others in case of both greyscale TEM and colored TEM images. Index Terms: TEM, Filter, SNR, PSNR I. INTRODUCTION A lot of work has been undertaken in the restoration and enhancement of ultrasound, MRI and other TEM images of different formats but the same efforts are yet to be made extensively for the transmission electron microscope (TEM) images. TEM images are rapidly gaining prominence in various sectors like life sciences, pathology, medical science, semiconductors, forensics, etc. Hence, there is a critical need to know the effect of existing image restoration and enhancement techniques on TEM images. There are multiple available techniques for improving the image quality. II. LITERATURE SURVEY The total variation has been introduced in Computer Vision first by Rudin, Osher and Fatemi [1], as a regularizing criterion for solving inverse problems. It has proved to be quite efficient for regularizing images without smoothing the boundaries of the objects. Antontonin proposed a relaxation method , an alternative method that was able to handle the minimization of the minimum of several convex functionals [2]. In 1995, an improvement to the choice of the regularization parameter involved in a deconvolution procedure was proposed. It was based on a statistical model allowing a good estimation of the spectral signal-to-noise ratio [3].Based on the CGM model, Chambolle (C) in [4] developed an efficient dual approach to minimize the scalar ROF model. C’s algorithm is faster than CGM even if the convergence of C’s scheme is linear and the CGM’s scheme is quadratic. C’s algorithm is faster because the cost per iteration to use CGM is higher (CGM needs to solve a linear system at each iteration). In 1999, a modified version of classical regularization techniques. Instead of using regularization in order to reduce the measurement noise effect of cancelling the inverse filter singularities, and to restore the original signal, a prefiltering was performed before the regularization. This prefiltering was obtained by using a Wiener filter based on a particular modelization of the signal to be restored [5]. A recent fast minimization algorithm for the scalar ROF model was proposed by Darbon and Sigelle (DS) in [6] based on graph cuts. Although C’s algorithm is not as fast as the model of DS to solve the variational scalar ROF model, it is still fast and presents some advantages compared with CGM and DS. First, C’s model use the exact scalar TV norm whereas CGM model regularizes it to minimize it. Then, the numerical scheme of [4] is straightforward to implement unlike the CGM and DS algorithms. Besides, the TV norm of DS is anisotropic whereas the TV norm of C is isotropic. Finally, we will see that the C’s model extends nicely to color/vector images whereas the question of extension is open for the CGM model and the generalization of DS model to color images is not as efficient as in the scalar case [7]. X. Bresson extended the Chambolle’s model [4] to multidimensional/vectorial images. Unlike the proposed vectorial scheme does not regularize the VTV to minimize it. Finally, the numerical solution converges to the continuous minimizing solution in the vectorial BV space. This VTV minimization scheme to several standard applications such as deblurring, inpainting, decomposition, denoising on manifolds [8]. Paul proposed a simple but flexible method for solving the generalized vector-valued TV (VTV) functional with a non negativity constraint. One of the main features of this recursive algorithm is that it is based on multiplicative updates only and can be used to solve the denoising and deconvolution problems for vector-valued (color) images [9]. In 2009, for image restoration, edge-preserving regularization method was used to solve an optimization problem whose objective function has a data fidelity term and a regularization term, the two terms are balanced by a parameter λ. In some aspect, the value of λ determines the quality of images. A new model to estimate the parameter and propose an algorithm to solve the problem was established. The quality of images was improved by dividing it into some blocks [10]. For the first time TV Regularization method was applied to fMRI data, and show that TV regularization is well suited to the purpose of brain mapping while being a powerful tool for brain decoding. Moreover, this article presents the first use of TV regularization for classification [11]. In the particular techniques, the SR problem is formulated by means of two terms, the data-fidelity term and the regularization term. The experimentation is carried out with the widely employed L2, L1, Huber and Lorentzian Garima Goyal / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5120-5124 www.ijcsit.com 5120