Many impulse noise (IN) reduction methods suffer from two obstacles, the improper noise detectors and imperfect filters they used. To address such issue, in this paper, a weighted couple sparse representation model is presented to remove IN. In the proposed model, the complicated relationships between the reconstructed and the noisy images are exploited to make the coding coefficients more appropriate to recover the noise-free image. Moreover, the image pixels are classified into clear, slightly corrupted, and heavily corrupted ones. Different data-fidelity regularizations are then accordingly applied to different pixels to further improve the denoising performance. In our proposed method, the dictionary is directly trained on the noisy raw data by addressing a weighted rank-one minimization problem, which can capture more features of the original data. Experimental results demonstrate that the proposed method is superior to several state-of-the-art denoising methods.

Weighted Couple Sparse Representation With Classified Regularization for Impulse Noise Removal

A comprehensive survey on impulse and Gaussian denoising filters for digital images

Impulse noise removal using SVM classification based fuzzy filter from gray scale images

In this study, a combination of adaptive vector median filter (VMF) and weighted mean filter is proposed for removal of high-density impulse noise from colour images. In the proposed filtering scheme, the noisy and non-noisy pixels are classified based on the non-causal linear prediction error. For a noisy pixel, the adaptive VMF is processed over the pixel where the window size is adapted based on the availability of good pixels. Whereas, a non-noisy pixel is substituted with the weighted mean of the good pixels of the processing window. The experiments have been carried out on a large database for different classes of images, and the performance is measured in terms of peak signal-to-noise ratio, mean squared error, structural similarity and feature similarity index. It is observed from the experiments that the proposed filter outperforms (~1.5 to 6 dB improvement) some of the existing noise removal techniques not only at low density impulse noise but also at high-density impulse noise.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2016.0320

Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images

Steganographic techniques can be utilized to conceal data within digital images with small or invisible changes in the perceived appearance of the image. Generally, five main objectives are used to assess the performance of steganographic algorithms which include embedding capacity, imperceptibility, security, robustness and complexity. However, steganographic algorithms hardly take all of these factors into account. In this paper, a novel steganographic algorithm for digital images is proposed based on the pixel-value differencing (PVD) and modified least-significant bit (LSB) substitution (MDPVD-MLSB) techniques to address most of aforementioned objectives. Although there are many techniques for concealing data within pixels, the restricting factor is always the amount of bits adjusted in every pixel. Therefore, the main contributions of this paper aim to achieve a balance between the amount of embedded data, the level of acceptable distortion, as well as providing high level of security. The performance of this algorithm has been extensively evaluated in terms of embedding capacity, peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Simulation results and comparisons with six relevant algorithms are presented to demonstrate the effectiveness of this proposed algorithm.DOI: http://dx.doi.org/10.5755/j01.itc.46.1.15253

/pdf/a-new-steganographic-algorithm-based-on-multi-directional-1qmz328v10.pdf

A New Steganographic Algorithm Based on Multi Directional PVD and Modified LSB

Switching median filters are known to outperform standard median filters in the removal of impulse noise due to their capability of filtering candidate noisy pixels and leaving other pixels intact. The boundary discriminative noise detection (BDND) is one powerful example in this class of filters. However, there are some issues related to the filtering step in the BDND algorithm that may degrade its performance. In this paper, we propose two modifications to the filtering step of the BDND algorithm to address these issues. Experimental evaluation shows the effectiveness of the proposed modifications in producing sharper images than the BDND algorithm.

R. A. AlNa'mneh

Papers

Efficient Improvements on the BDND Filtering Algorithm for the Removal of High-Density Impulse Noise