The Removal of Random Valued Impulse Noise Using Contrast Enhancement and Decision Based Filter

Digital images are transmitted in noisy environment and it will frequently affected by impulse noise .To remove this noise from the image is a fundamental problem of image processing. There are various types of noise in an image especially salt and pepper noise and random valued impulse noise. This paper introduces a new filtering scheme based on contrast enhancement filter and decision based filter for removing the random valued impulse noise. The application of a nonlinear function to increasing the difference between noise pixels and noise-free and results in efficient detection of noisy pixels. As the performance of a filtering system, in general, depends on the number of iterations used, the effective stopping criterion based on noisy image characteristics to determine the number of iterations is also proposed. This proposed method removes only the corrupted pixel by its neighboring pixel values. As a result of this, the proposed method removes the noise effectively and preserves the edges without any loss up to 80% of noise level

An enhanced fletcher-reeves-like conjugate gradient methods for image restoration

Noise is an unavoidable aspect of modern camera technology, causing a decline in the overall visual quality of the images. Efforts are underway to diminish noise without compromising essential image features like edges, corners, and other intricate structures. Numerous techniques have already been suggested by many researchers for noise reduction, each with its unique set of benefits and drawbacks. Denoising images is a basic challenge in image processing. We describe a two-phase approach for removing impulse noise in this study. The adaptive median filter (AMF) for salt-and-pepper noise identifies noise candidates in the first phase. The second step minimizes an edge-preserving regularization function using a novel hybrid conjugate gradient approach. To generate the new improved search direction, the new algorithm takes advantage of two well-known successful conjugate gradient techniques. The descent property and global convergence are proven for the new methods. The obtained numerical results reveal that, when applied to image restoration, the new algorithms are superior to the classical fletcher reeves (FR) method in the same domain in terms of maintaining image quality and efficiency

Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images

This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. © 2004 IEEE.In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the sec ond filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters.This work was supported in part by CenSSIS, the Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (NSF) under Award EEC-9986821, by an ARO MURI on Demining under Grant DAAG55-97-1-0013, and by the NSF under Award 0208548

Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, and many others. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part respectively. Specifically, for the clean HSI part, we use tensor Tucker decomposition to describe the global correlation among all bands, and an anisotropic spatial-spectral total variation (SSTV) regularization to characterize the piecewise smooth structure in both spatial and spectral domains. For the mixed noise part, we adopt the $\ell_1$ norm regularization to detect the sparse noise, including stripes, impulse noise, and dead pixels. Despite that TV regulariztion has the ability of removing Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian noise for some real-world scenarios. Then, we develop an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier (ALM) method. Finally, extensive experiments on simulated and real-world noise HSIs are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones.Comment: 15 pages, 20 figure

Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal

Most existing image denoising algorithms can only deal with a single type of noise, which violates the fact that the noisy observed images in practice are often suffered from more than one type of noise during the process of acquisition and transmission. In this paper, we propose a new variational algorithm for mixed Gaussian-impulse noise removal by exploiting image local consistency and nonlocal consistency simultaneously. Specifically, the local consistency is measured by a hyper-Laplace prior, enforcing the local smoothness of images, while the nonlocal consistency is measured by three-dimensional sparsity of similar blocks, enforcing the nonlocal self-similarity of natural images. Moreover, a Split-Bregman based technique is developed to solve the above optimization problem efficiently. Extensive experiments for mixed Gaussian plus impulse noise show that significant performance improvements over the current state-of-the-art schemes have been achieved, which substantiates the effectiveness of the proposed algorithm.Comment: 6 pages, 4 figures, 3 tables, to be published at IEEE Int. Conf. on Multimedia & Expo (ICME) 201