Alberto J. Rosales-Silva

A fuzzy clustering algorithm with spatial robust estimation constraint for noisy color image segmentation

In this paper, we introduce two enhanced Fuzzy C-Means (FCM) clustering algorithms with spatial constraints for noisy color image segmentation. The Rank M-type L (RM-L) and L-estimators are used to obtain the sufficiently spatial information of the pixels. These estimators are involved into the FCM algorithm to provide robustness for the proposed segmentation schemes. The performance of the proposed algorithms is tested in real images under different noise conditions by simulating salt and pepper, Gaussian, and speckle noises, as well as with two mixtures of them. Simulation results indicate that the proposed methods consistently outperform other color image segmentation algorithms used as comparative. Additionally, the proposed algorithms are tested for segmenting a remote sensing image, where the noise is not known beforehand implied. Finally, the proposed algorithms have the robustness and effectiveness needed for image segmentation in the presence and absence of noise.

Fuzzy Directional (FD) Filter for impulsive noise reduction in colour video sequences

This paper presents a novel Fuzzy Directional (FD) Filter for suppression of impulsive noise in colour video sequences. The proposed approach consists in the estimation of fuzzy levels to detect movement and noise presence in the neighbourhood frames, permitting to preserve the edges, fine details and chromaticity characteristics in colour images and video sequences. The new framework has been justified applying commonly used objective criteria, such as, Peak Signal to Noise Ratio (PSNR), Mean Absolute Error (MAE) and Normalized Colour Difference (NCD), as well subjective perception by human viewer showing better performance in comparison with known methods presented in the literature.

Real-Time Color Image Processing Using Order Statistics Filters

The Vector Rank M-type K-Nearest Neighbour (VRMKNN) filter to remove impulsive noise from color images and video color sequences is presented. This filter utilizes multichannel image processing by using the vector approach and the Rank M-Type K-Nearest Neighbour (RMKNN) algorithm. Simulation results indicate that the proposed filter consistently outperforms other color image filters by balancing the tradeoff between noise suppression and detail preservation. The implementation of the filter was realized on the DSP TMS320C6711 to demonstrate that the proposed filter potentially could provide a real-time solution to quality video transmission.

Color local complexity estimation based steganographic (CLCES) method

In this paper we present the color local complexity estimation based steganographic (CLCES) method that is able of both preventing visual degradation and providing a large embedding capacity. A preprocessing stage is applied in the proposed scheme to improve the steganography security. The embedding capacity of each pixel is determined by the local complexity of the cover image, allowing good visual quality as well as embedding a large amount of secret messages. We classify the pixels using a threshold based on the standard deviation of the local complexity in the cover image to provide a compromise between the embedding capacity and the image visual quality. The experimental results demonstrated that the algorithm CLCES proposed produces insignificant visual distortion due to the hidden message. It provides a high embedding capacity that is superior respect to the offered by the existing schemes. The proposed method is a secure steganographic algorithm; it can resist the image quality measures (IQM) steganalysis attack. The RGB, YCbCr, and HSV color spaces are incorporated in the proposed scheme to ensure that the difference between the cover image and the stego-image which is indistinguishable by the human visual system (HVS). Finally, the proposed scheme is simple, efficient, and feasible for the adaptive steganographic applications.

3D Filtering of Colour Video Sequences Using Fuzzy Logic and Vector Order Statistics

Novel approach designed in this paper permits the suppression of impulsive noise in multichannel video sequences. It employs the fuzzy logic and vector order statistic methods to detect motion and noise presence during spatial-temporal processing neighbouring video frames, preserving the edges, fine details, as well as colour properties. Numerous simulation results have justified it excellent performance in terms of objective criteria: Pick Signal-to- Noise Ratio (PSNR), Mean Absolute Error (MAE) and Normalized Colour Difference (NCD), as well as in subjective perception by human viewer.

Short Communication: Multichannel image processing by using the Rank M-type L-filter

In this paper, we introduce the Vector Rank M-type L (VRML)-filter to remove impulsive noise from color images and video sequences. The proposed filter uses the Median M-type (MM) and Ansari-Bradley-Siegel-Tukey M-type (AM) estimators into L-filter to provide robustness to proposed filtering scheme. We also introduce the use of impulsive noise detectors to improve the properties of noise suppression and detail preservation in the proposed filtering scheme in the case of low and high densities of impulsive noise. Simulation results indicate that the proposed filter consistently outperforms other color image filters by balancing the trade-off between noise suppression, detail preservation, and color retention.

Robust fuzzy scheme for Gaussian denoising of 3D color video

We propose a three-dimensional Gaussian denoising scheme for application to color video frames. The time is selected as a third dimension. The algorithm is developed using fuzzy rules and directional techniques. A fuzzy parameter is used for characterization of the difference among pixels, based on gradients and angle of deviations, as well as for motion detection and noise estimation. By using only two frames of a video sequence, it is possible to efficiently decrease Gaussian noise. This filter uses a noise estimator that is spatio-temporally adapted in a local manner, in a novel way using techniques mentioned herein, and proposing a fuzzy methodology that enhances capabilities in noise suppression when compared to other methods employed. We provide simulation results that show the effectiveness of the novel color video denoising algorithm.

Robust c-prototypes algorithms for color image segmentation

In this paper, we present a modified clustering algorithm to segment color images. The proposed technique is based on the robust c-prototypes algorithm with some modifications in its objective function and updating equations. The JK chromatic subspace of the IJK color space is used to segment color images. The algorithm performance is tested on real images with natural artifacts that make the segmentation process difficult. Additionally, a remote sensing image is segmented to demonstrate that the proposed algorithm can be used in real applications. Simulation results indicate that the proposed method is more effective than others proposed in the literature in terms of objective and subjective criteria.

Variance field estimation-based steganographic (VFES) method for RGB colour images

Abstract A variance field estimation-based steganographic method for RGB colour images is presented. The proposed method uses variance field estimation and the redundancy of approaches algorithm to provide good invisibility and fine detail preservation in the processed stego-images. Experimental results show that the proposed method is a secure steganographic method that provides good image quality and hiding capacity.

3D vector directional filters to process video sequences

We present novel algorithms to suppress impulsive noise in video color sequences. They use order statistics, directional and adaptive processing techniques. Extensive simulation results in known reference video color sequences have demonstrated that the proposed filters consistently outperform other filters by balancing the tradeoff between noise suppression, detail preservation, and chromaticity characteristics. The criteria used to compare the performance or various filters were the PSNR, MAE, and NCD.