T. Veerakumar

Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter

A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0s and 255s are present in the selected window and when all the pixel values are 0s and 255s then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

Salt and pepper noise removal in video using adaptive decision based median filter

This paper proposes a new algorithm to remove salt and pepper noise in video. The adaptive decision algorithm first checks whether the selected pixel in the video sequence is noisy or noise free. Initially the window size is selected as 3 × 3. If the selected pixel within the window is 0s or 255s, and some of other pixels within the window are noise free, then the selected pixel value is replaced by trimmed median value. If the selected pixel is 0 or 255 and other pixel values in a selected window (3 × 3) all are 0s and 255s, then change the selected window size as 5 × 5, then the selected pixel value is replaced by trimmed median value. In the selected new window (5 × 5), all the elements are 0s or 255s then the processing pixel is replaced by the previous resultant pixel. Finally, the performance of the proposed algorithm is compared with the existing algorithms like Median Filter; Decision Based Filter and Progressive Switched Median Filter. The proposed algorithm gives better PSNR and IEF results than the existing algorithms.

Recursive cubic spline interpolation filter approach for the removal of high density salt-and-pepper noise

Spline-based approach is proposed to remove very high density salt-and-pepper noise in grayscale and color images. The algorithm consists of two stages, the first stage detects whether the pixel is noisy or noise-free. The second stage removes the noisy pixel by recursive spline interpolation filter. The proposed recursive spline interpolation filter is based on the neighborhood noise-free pixels and previous noise-free output pixel; hence, it is termed as recursive spline interpolation filter. The performance of the proposed algorithm is compared with the existing algorithms like standard median filter, decision-based filter, progressive switched median filter, and modified decision-based unsymmetric trimmed median filter at very high noise density. The proposed algorithm gives better peak signal-to-noise ratio, image enhancement factor, and correlation factor results than the existing algorithms.

An efficient approach to remove random valued impulse noise in images

In this paper, a new algorithm is introduced to remove the random valued impulse noise in images. This algorithm contains two stages. The first stage detects the noisy pixels in the image. In the second stage, the noisy pixel is replaced by the median value of the neighborhood noise free pixels. The absolute difference is used to detect the noisy pixel and trimmed median value replaces the noisy pixel. This proposed algorithm shows better results than the Progressive Switching Median Filter (PSM), Pixel-wise Median Absolute Difference (PWMAD), Tristate median filter (TSM), Efficient Procedure for removing Random Valued Impulse Noise (EPRIN) and Optimal Direction Based random valued impulse noise (ODRIN). The proposed algorithm is tested for different gray scale images and it gives better Peak Signal to Noise Ratio.

An Approach to Minimize Very High Density Salt and Pepper Noise through Trimmed Global Mean

In this paper, we are proposing an approach to minimize very high density salt and pepper noise through trimmed global mean. The Modified decision based unsymmetrical trimmed median filter tries to remove high density salt and pepper noise by taking the mean value of the elements within the processing window. This algorithm fails if all the elements within the processing window is either ‘0’ or ‘255’. In our approach, if all the elements within the window are ‘0’ or ‘255’ then the noisy pixel is replaced by the trimmed global mean. The proposed algorithm exhibits better image quality than the median filter, adaptive median filter, decision based median filter and modified decision based unsymmetrical trimmed median filter. The proposed algorithm is tested for different grayscale and color images and it gives better peak signal to noise ratio and image enhancement factor.

Adaptive vector quantization based video compression scheme

This paper presents a new video compression technique which is based on adaptive vector quantization of multiwavelet coefficients. Three types of redundancies that are common in video sequences are spatial, temporal and psycho visual redundancies. In this work, the spatial redundancy is minimized using Multiwavelet transform, temporal redundancy is minimized using Kite Cross Diamond Search motion estimation algorithm, and the psycho visual redundancy is minimized using adaptive vector quantization technique. The objective of the paper is to develop a low bit rate video coder with acceptable visual quality. The performance of the proposed scheme is compared with wavelet based video coder. Simulation results show that multiwavelet based adaptive vector quantization gives better coding performance than wavelet based adaptive vector quantization scheme.

Best Basis Selection Using Singular Value Decomposition

This paper presents a new idea of best basis selection through singular value decomposition. Wavelet and Wavelet Packet Transform are efficient tools to represent the image. Wavelet Packet Transform is a generalization of wavelet transform which is more adaptive than the wavelet transform because it offers a rich library of bases from which the best one can be chosen for a certain class of images with a specified cost function. Wavelet packet decomposition yields a redundant representation of the image. The problem of wavelet packet image coding consists of considering all possible wavelet packet bases in the library, and choosing the one that gives the best coding performance. In this work, Singular Value Decomposition is used as a tool to select the best basis. Experimental results have demonstrated the validity of the approach.

Impulse Noise Removal Using Adaptive Radial Basis Function Interpolation

A novel adaptive radial basis function interpolation-based impulse noise removal algorithm is introduced in this manuscript. This approach consists of two stages: noisy pixel detection and correction. In former step, the noise-affected pixels in an image are detected, and in the latter step, the noisy pixels are restored by adaptive radial basis function-based interpolation scheme. The radial basis function interpolation scheme is used to estimate the unknown noisy pixel value from the noise-free known neighboring pixel values. For both noisy pixel detection and correction, a center sliding window is considered at each pixel location. The proposed approach is experimented on some benchmark data sets, and its performance is evaluated using five performance evaluation measures: PSNR, MSSIM, IEF, correlation factor, and NSER on different test images by comparing it against sixteen different state-of-the-art techniques. It is found that the proposed approach gives better results than the sixteen different state-of-the-art techniques.

Image Compression using Adaptive Wavelet Packet and Multistage Vector Quantization

This paper presents a new image coding technique using adaptive wavelet packet and multistage vector quantization. Wavelet packets are generalization of wavelet transform, capable of providing arbitrary frequency resolution to meet signals spectral behavior. Image properties, filter and cost function are the three prime factors which are commonly used to select wavelet packet basis. In this paper, the best basis is selected through singular value decomposition. After selecting the best tree, the coefficients of the best tree are quantized using multistage vector quantization. Experimental results show that wavelet packet transform brings consistent improvement over dyadic wavelet transform.

Context model based edge preservation filter for impulse noise removal

Absolute difference with line detectors for impulse noise identification.Edge preserving context model is used for impulse noise correction.Gaussian kernel is used for edge preserving of the processing pixel. In this article, a new edge preserving contextual model based image restoration technique is proposed for images affected by impulse noise. The proposed restoration technique consists of two stages: noisy pixel identification and restoration. Center sliding window is considered as current processing pixel for both noisy pixel identification and restoration. In the first stage of the proposed technique, we follow an absolute directional difference of the neighborhood pixels to identify the pixels those are affected by impulse noise. We propose an edge preserving contextual model to restore the noisy pixels. The noise correction stage of the proposed scheme depends on the context model of the noise-free pixels in the selected window. The parameters of the contextual model are obtained using a Gaussian kernel. The proposed algorithm is tested on nine benchmark test images. The evaluation of the proposed algorithm is carried out by comparing it against nine competitive state-of-the-art algorithms for impulse noise removal. The proposed algorithm is evaluated using Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM), Non-shifted Edge Ratio (NSER) and Correlation Factor (CF) performance measures. Experimental results corroborate that the proposed algorithm provides better performance than the existing state-of-art impulse denoising methods.