Volodymyr Ponomaryov

Real-time image filtering scheme based on robust estimators in presence of impulsive noise

This paper presents the capability and real-time processing features of a robust filter for the removal of impulsive noise in image processing applications. The real-time implementation of image filtering was realized on the DSP TMS320C6701. Extensive simulation results with different images have demonstrated that the proposed filter consistently outperforms other filters by balancing the tradeoff between noise suppression and fine detail preservation. We simulated impulsive corrupted video sequences to demonstrate that the proposed method potentially could provide a real-time solution to quality video transmission.

Super Resolution Image Generation Using Wavelet Domain Interpolation With Edge Extraction via a Sparse Representation

This letter addresses the problem of generating a super-resolution (SR) image from a single low-resolution (LR) input image in the wavelet domain. To achieve a sharper image, an intermediate stage for estimating the high-frequency (HF) subbands has been proposed. This stage includes an edge preservation procedure and mutual interpolation between the input LR image and the HF subband images, as performed via the discrete wavelet transform (DWT). Sparse mixing weights are calculated over blocks of coefficients in an image, which provides a sparse signal representation in the LR image. All of the subband images are used to generate the new high-resolution image using the inverse DWT. Experimental results indicated that the proposed approach outperforms existing methods in terms of objective criteria and subjective perception improving the image resolution.

Real-time 2D-3D filtering using order statistics based algorithms

The paper presents a review of the author’s own results obtained in the last several years. Some examples of real-time processing of 2D and 3D images are described. In particular, we discuss the noise model and objective criteria that can be applied to characterize the performance of the processing algorithms. Several proposed algorithms based on RM approach are compared with other known ones, demonstrating the advantages in noise suppressing and preservation of fine image details and edges. A number of 2D and 3D image denoising filters are implemented on DSP, realizing real-time mode in the image processing. The performances of the proposed processing algorithms and the known ones are discussed and evaluated here.

Wavelet Transform Fuzzy Algorithms for Dermoscopic Image Segmentation

This paper presents a novel approach to segmentation of dermoscopic images based on wavelet transform where the approximation coefficients have been shown to be efficient in segmentation. The three novel frameworks proposed in this paper, W-FCM, W-CPSFCM, and WK-Means, have been employed in segmentation using ROC curve analysis to demonstrate sufficiently good results. The novel W-CPSFCM algorithm permits the detection of a number of clusters in automatic mode without the intervention of a specialist.

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.

Adaptive Vector Directional Filters to Process Multichannel Images

We present a novel algorithm that can suppress impulsive noise in video colour sequences. It uses order statistics, and directional and adaptive processing techniques.

ABST M-Type K-Nearest Neighbor (ABSTM-KNN) for Image Denoising

We present the Ansari-Bradley-Siegel-Tukey M-type K-nearest neighbor (ABSTM-KNN) filter to remove impulse noise from corrupted images. Extensive simulations have demonstrated that the proposed filter consistently outperforms other filters by balancing the tradeoff between noise suppression and detail preservation.

Fuzzy 3D filter for color video sequences contaminated by impulsive noise

A novel fuzzy 3D filter designed to suppress impulsive noise in color video sequences is proposed. In contrast to other state-of-the-art algorithms, the proposed approach employs the sequence data of the three RGB channels, analyzes eight fuzzy gradient values for each of the eight directions, and processes two temporal neighboring frames concurrently. Numerous simulation results confirm that this novel 3D framework performs well in terms of objective criteria (PSNR, MAE, NCD, and SSIM) and the more subjective measure of human vision in the different color sequences. An efficiency analysis of several promising 3D algorithms was performed on a DSP; computation times for various techniques are presented.

Algorithms of Three-Dimensional Filtration Using the Fuzzy-Set Theory for Color Image Sequences Degraded by Noise

363 1. In this study, we propose and justify original algorithms of three-dimensional (3D) space–time filtration employing the ideas presented in [1–5]. The algorithms are based on the theory of fuzzy sets [2–5], which represent a new class of nonlinear filters used for eliminating the effect of additive noise in video images. The proposed algorithms use the values of multichannel pixels and angular differences between them for jointly filtering neighboring images on the basis of new fuzzy-logic rules, which make it possible to select pixels of similar structure, thus significantly increasing the processed sample volume and improving the filtration quality. Criteria used in describing and comparing the known and proposed algorithms are the peak signalto-noise ratio (PSNR) in decibels, the mean absolute error (MAE) determining the quality of reconstruction of fine details in the image, and also the chromatic parameters such as, for example, the normalized chromatic difference (NCD) and the mean chromatic ratio error (MCRE) [6–8]. 2. The video image sequence filtration begins with the first image in the sliding processing 3 × 3 window, where the angular deviations θ c = D ( , x c ) of pixels with respect to the central x c value in the window are determined on the basis of the average value ( β = (Red, Green, Blue) for color images). Using a histogram constructed for the image, the mean value, the