We propose a stationary and discrete wavelet based image denoising scheme and an FFTbased image denoising scheme to remove Gaussian noise. In the first approach, high subbands are added with each other and then soft thresholding is performed. The sum of low subbands is filtered with either piecewise linear (PWL) or Lagrange or spline interpolated PWL filter. In the second approach, FFT is employed on the noisy image and then low frequency and high frequency coefficients are separated with a specified cutoff frequency.Then the inverse of low frequency components is filtered with one of the PWL filters and the inverse of high frequency components is filtered with soft thresholding. The experimental results are compared with Liu and Liu's tensor-based diffusion model (TDM) approach.
We propose an image enhancement scheme by using YCBCR color space method. It shows the better feature of the processed input image. The acquired images are classified into three types, word document image, MRI image and scenery image. At first, the acquired inputs are converted to the gray scale to plot with the normalized histogram. Then, using the color space methods, the images are converted into YCBCR characteristics and there components are separated into individual modules(Y, CB, CR components). The processed image separates its in-features of luminance and chrominance components such as Y component, CB component and CR component. In Gray scale image, the Y is said to be the luminance feature also known as single component. In Color image, CB and CR is said to be the chromaticity of blue and red components. Further we find Hue, Saturation and Intensity components are classified from the same samples. Then the proposed technique shows its better performance than the other methods in the enhancement of images corrupted by Gaussian noise. The Experimental result shows that the proposed methods makes good enhancement in visual quality.
Abstract-This study is focused on histogram thresholding methods automatically. In computer vision, Image segmentation is an initial and vital step in a series of processes aimed at overall image understanding. In other words Segmentation refers to the process of partitioning a digital image into the multiple segments (set of pixels as known as super pixels). Two very simple image segmentation techniques that are based on the gray level histogram of an image are Thresholding and Clustering. Thresholding method is widely used for image segmentation approach. It is useful in discriminating foreground from the background.By selecting an adequate threshold value T, or automatically computing threshold value T, the gray level image can be converted in to binary image. Several methods are there to find the threshold automatically for image segmentation. Some of the methods like Otsu, Kapur, Triangle, Iterative and also manually threshold is calculated for different type of images like X-ray computed tomography (CT-Scan), magnetic resonance imaging (MRI), synthetic aperture radar (SAR), Ultrasound image were explained and the results are presented to show the validity of the methods.
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