Detail Preservation Capability Research Articles

A Low-Rank and Sparse Decomposition-Based Method of Improving the Accuracy of Sub-Pixel Grayscale Centroid Extraction for Spot Images

In order to improve the accuracy of sub-pixel grayscale centroid extraction for noise-containing spot images, an image denoising method based on low-rank and sparse decomposition (LRSD) was proposed in this paper. Relative total variation (RTV) was introduced into the method on the basis of the weighted nuclear norm minimization (WNNM) model to construct a new RTV-WNNM model, so as to enhance the denoising accuracy and detail-preserving capability of LRSD method. Alternating direction multiplier method (ADMM) was used to solve this convex problem iteratively. Moreover, in view that the existing LRSD denoising method had insufficient detail-preserving capability, an adaptive selection method of LRSD singular value threshold based on the improved elitist clone selection algorithm (ECSA) was also raised in this paper. The constructed RTV-WNNM model taken as the objective function, the improved ECSA could realize equilibrium between search in depth and optimization in breadth through the adaptive chaotic mutation operator and antibody reselection strategy based on antibody concentration and antigen affinity moment. Furthermore, it could reach rapid self-adaption to acquire a low-rank threshold and then form an image denoising fusion algorithm, which had both denoising and detail-preserving capabilities. Simulation and experimental results show that the proposed method can reach better results in both quantity measure and visual quality than the state-of-the-art image denoising methods, and it has also remarkably improved the accuracy of sub-pixel grayscale centroid extraction for noise-containing spot images.

Performance Study of Novel Median Filter for Image De-Noising

Most common method of clearing image noise is through median filtration. Median smoothers discard outliers (or impulses) quite effectively at lower noise densities but, fail to provide adequate smoothing for heavily noised images for the simple reason that, in the conventional median filter approaches, each and every pixel of an image is filtered without concern about healthy pixels. To suppress this deficiency, in this paper, an improved median filter by the name Novel Median Filter (NMF) is proposed to enhance the effectiveness of the median filter at higher noise situations to provide a detail preserving de-noising of digital images. Proposed NMF algorithm is a 3-stage filter; first two stages are dedicated to selective switching median filtration in which noisy pixels are first detected and then the filtering operation is applied to restore their values with a valid (or a noise free) median computed for each 3×3 window position. Third level processing enables to remove any distortions present in the de-noised image. The proposed method, Novel Median Filter (NMF) is evaluated for its effectiveness in detail preserving de-noising capability on a large number of digital grey and color images affected with impulsive and non-impulsive noise in terms of objective and subjective quality measures. To better appraise the efficacy of the proposed method, Novel Median Filter (NMF), MATLAB simulation results in terms of peak signal to noise ratio (PSNR), mean square error (MSE) and the de-noised images are obtained and are compared with those obtained for a standard median filter (SMF) and its few variants and meaningful conclusions are derived. Detail preservation capability of the proposed filter has been demonstrated through experimentation on medical samples. General Terms Image Processing, Nonlinear Filter, Median Filtration.

Switching non-local median filter

This paper describes a novel image filtering method for removal of random-valued impulse noise superimposed on grayscale images. Generally, it is well known that switching-type median filters are effective for impulse noise removal. In this paper, we propose a more sophisticated switching-type impulse noise removal method in terms of detail-preserving performance. Specifically, the noise detector of the proposed method finds out noise-corrupted pixels by focusing attention on the difference between the value of a pixel of interest (POI) and the median of its neighboring pixel values, and on the POI’s isolation tendency from the surrounding pixels. Furthermore, the removal of the detected noise is performed by the newly proposed median filter based on non-local processing, which has superior detail-preservation capability compared to the conventional median filter. The effectiveness and the validity of the proposed method are verified by some experiments using natural grayscale images.

Fuzzy-based hybrid filter for Rician noise removal

Magnetic resonance images tend to be contaminated with random unwanted signals called noise, due to various reasons. Noise treatment of magnetic resonance brain images is considered as an important and challenging task for proper clinical and research investigations. In this manuscript, fuzzy logic-based hybrid Rician noise filter has been proposed. Proposed filtering technique uses estimated noise variance along with local and global statistics for the construction of a robust fuzzy membership function. Constructed fuzzy membership function assigns appropriate weights to the statistical estimates, based on their noise removal and detail preservation capability. Fuzzy weighted local and non-local estimators are then used for the restoration of a noisy pixel. Detailed simulations are performed, and restoration results are computed based on well-known performance measures. Numerical and visual results show that the proposed technique gives much better restored images than the existing methodologies.

Discontinuity Adaptive Non-Local Means With Importance Sampling Unscented Kalman Filter for De-Speckling SAR Images

This paper brings out two extensions/utilities of well-known non-local means filter. Firstly, we propose an improvement to basic non-local means filter (NLMF) by incorporating discontinuity adaptive weights, instead of Gaussian weights for similarity assessment of patches in NLMF. As against the NLMF, discontinuity-adaptive NLMF (DA-NLMF) exhibit better edge and detail preserving capability, while providing very effective de-noising at the homogeneous areas. Secondly, we propose to apply DA-NLMF on recently proposed Importance Sampling Unscented Kalman Filter (ISUKF) [1], which can also be thought of as an elegant post processing technique, in general. Specifically, we propose an efficient method for de-speckling Synthetic Aperture Radar imagery by combining ISUKF and DA-NLMF. In our approach, the DA-NLMF provides an efficient de-speckling as well as feature preservation, when its parameters of its (DA) weighting function are derived from ISUKF results. The performance of these methods is demonstrated on both synthetic and real examples, and the proposed method gives excellent results than the standard speckle filtering methods as well as various advanced methods.

Fuzzy based Impulse Noise Reduction Method

In this paper, we propose an image filtering technique based on fuzzy logic control to remove impulse noise for low as well as highly corrupted images. The proposed method is based on noise detection, noise removal and edge preservation modules. The main advantage of the proposed technique over the other filtering techniques is its superior noise removal as well as detail preserving capability. Based on the criteria of peak-signal-to-noise-ratio (PSNR), mean square error (MSE), structural similarity index measure (SSIM) and subjective evaluation measure we have found experimentally that the proposed method provides much better performance than the state-of-the-art filters. To analyze the detail preservation capability of the proposed filter sensitivity analysis is performed by changing the detail preservation module to see its effects on the details (texture and edge information) of resultant image. This sensitivity analysis proves experimentally that significant image details have been preserved by the proposed method.

A Novel Recursive Algorithm For Detail-Preserving Impulse Noise Removal

Removing impulse noise from digital images without altering image details is a central problem in image processing. There is a huge number of filters in the literature that deal with this problem. In the state-of-the-art filters, the removal of impulse noise is usually carried out in two main steps: detection and estimation. In this paper, we show that we can take any impulse noise removal approach, where the noise detection and estimation processes are separable, and enhance its detail preservation capability. Unlike the classical recursive implementation which performs sequential row-by-row scanning, the proposed algorithm maximizes the contribution of noise-free neighbors in detecting and correcting the noisy pixels. Simulation results using standard test images demonstrate the effectiveness of the proposed algorithm in preserving image details.

A Statistically-Switched Adaptive Vector Median Filter

This paper presents a new cost-effective, adaptive multichannel filter taking advantage of switching schemes, robust order-statistic theory and approximation of the multivariate dispersion. Introducing the statistical control of the switching between the vector median and the identity operation, the developed filter enhances the detail-preserving capability of the standard vector median filter. The analysis and experimental results reported in this paper indicate that the proposed method is capable of detecting and removing impulsive noise in multichannel images. At the same time, the method is computationally efficient and provides excellent balance between the noise attenuation and signal-detail preservation. Excellent performance of the proposed method is tested using standard test color images as well as real images related to emerging virtual restoration of artworks.

ℒ-M-S filters for image restoration applications

A new filtering architecture is proposed, generalizing some previously introduced multilevel median filters. An efficient design procedure for the new filtering architecture is demonstrated for image restoration application. Simulation results show a good noise rejection performance, combined with a fine detail preservation capability.

Three-dimensional median-related filters for color image sequence filtering

Most current algorithms developed for image sequence filtering require motion information in order to obtain good results both in the still and moving parts of an image sequence. In the present paper, filters which completely preserve stationary regions in image sequences are introduced. In moving regions, the 3D filters inherently reduce to spatial filters and perform well in these areas without any motion-compensation or motion-detection. A new multivariate filtering operation called the alpha-trimmed vector median is proposed. Guidelines for the determination of optimal 3D median-related structures for color and gray-level image sequence filtering are given. Algorithms based on vector median, extended vector median, alpha-trimmed vector median, and componentwise median operations are developed. Properties of the human visual system are taken into account in the design of filters. Noise attenuation and detail preservation capability of the filters is examined. In particular, the impulsive noise attenuation capability of the filters is analyzed theoretically. Simulation results based on real image sequences are given. >