Noisy image edge detection is one of the major areas of work in image processing. Edge detection of the noise-free images is much easier than finding the edge of noisy images. But in practice, many of the images are imbued with noise. This paper will present a new algorithm based on morphology operation to eliminate noise and perform correct edge detection simultaneously. The algorithm is appli...

Usual segmentation techniques of grayscale images depend on supervised trial-and-error procedures. Moreover, in noisy images, local classi ̄cation schemes fail due to the random °uctuations introduced by the noise. Recent proposals as the active contours may be robust enough to cope with some cases of noisy images without supervision (except for the initialization step), but still fail with imag...

Most of existing image denoising methods learn image priors from either external data or the noisy image itself to remove noise. However, priors learned from external data may not be adaptive to the image to be denoised, while priors learned from the given noisy image may not be accurate due to the interference of corrupted noise. Meanwhile, the noise in real-world noisy images is very complex,...

This chapter presents a statistical analysis of multiscale derivative measurements. Noisy images and multiscale derivative measurements made of noisy images are analyzed; the means and variances of the measured noisy derivatives are calculated in terms of the parameters of the probability distribution function of the initial noise function and the scale or sampling aperture. Normalized and unno...

periodic noises are unwished and spurious signals that create repetitive pattern on images and decreased the visual quality. firstly, this paper investigates various methods for reducing the effects of the periodic noise in digital images. then an adaptive optimum notch filter is proposed. in the proposed method, the regions of noise frequencies are determined by analyzing the spectral of noisy...

Periodic noises are unwished and spurious signals that create repetitive pattern on images and decreased the visual quality. Firstly, this paper investigates various methods for reducing the effects of the periodic noise in digital images. Then an adaptive optimum notch filter is proposed. In the proposed method, the regions of noise frequencies are determined by analyzing the spectral of noisy...

A major problem in noisy image processing is the effective segmentation of its components. In this paper, we are proposing a K Medoid clustering algorithm for noisy image segmentation, which is able to segment all types of noisy images efficiently. As the presented clustering algorithm selects the centroids randomly hence it is less sensitive , to any type of noise as compare to other clusterin...

Wiener filter suppresses noise efficiently. However, it makes the out image blurred. Curvelet preserves the edges of natural images perfectly, but, it produces visual distortion artifacts and fuzzy edges to the restored image, especially in homogeneous regions of images. In this paper, a new image denoising framework based on Curvelet transform and wiener filter is proposed, which can stop nois...