Impulse Noise In Digital Images Research Articles

Modified convolutional neural network with pseudo-CNN for removing nonlinear noise in digital images

Recently, deep learning techniques are widely used in various computer vision applications such as pattern recognition, data classification, object detection, image enhancement, etc. Deep learning frameworks outperform classical algorithms due to their flexibility and interoperability. The main aim of this work is to remove Gaussian-Impulse noise with blind (unknown) noise densities in digital images. The proposed work has two phases; in phase 1, the noisy image is preprocessed with a novel Pseudo–Convolutional Neural Network (P-CNN) having no tunable parameters. The Pseudo-CNN is a customized three-layered network inspired by conventional convolutional neural networks. The first-level feature detector has k number of (2d+1)×(2d+1) Weight Initialized Adaptive Window (WIAW) filters. The weights or coefficients of WIAW filters are initialized using the noise probabilistic distribution. The loss function is not estimated in P-CNN because there is only one forward pass and no backpropagation. The PCNN has excellent Impulse noise rejection capability. In phase 2, a modified Convolutional Neural Network (CNN) is applied to the preprocessed image to obtain a latent clean image. To improve the network performance, we have utilized residual learning and batch normalization. The high degree of localized pixel correlation established by P-CNN helps the modified CNN to learn compact representations and salient features of the input image. The proposed method not only denoises images corrupted with blind noise levels of Gaussian-Impulse noise but is also effective for removing Impulse noise in digital images. Our experimental results imply that the proposed method gives good qualitative and quantitative results compared to various state-of-the-art techniques. Convolutional neural networks are pertinent for parallel computation using powerful GPUs, which help to improve the denoising performance.

Adaptive impulse detection based selective window median filter for removal of random-valued impulse noise in digital images

Purpose One of the fundamental tasks in the field of image processing is image denoising. Images are often corrupted by different types of noise and the restoration of images degraded with random-valued impulse noise is still a challenging task. The paper aims to discuss these issues. Design/methodology/approach This paper presents an adaptive threshold-based impulse noise detection following by a novel selective window median filter for restoration of RVIN pixels. Findings The proposed method emphasis a local image statistics using an exponential nonlinear function with an adaptive threshold is derived from the rank-ordered trimmed median absolute difference (ROTMAD) are deliberated to detect the noisy pixels. In the filtering stage, a selective 3×3 moving window median filter is applied to restore the detected noisy pixel. Originality/value Experimental result shows that the proposed algorithm outperforms the existing state-of-art techniques in terms of noise removal and quantitative metrics such as peak signal to noise ratio (PSNR), mean absolute error (MAE), structural similarity index metric (SSIM) and miss and false detection rate.

Removal of impulse noise in digital images with na\"ıve Bayes classifier method

Removal of impulse noise in digital images with na\"ıve Bayes classifier method

Reduction in impulse noise in digital images through a new adaptive artificial neural network model

In this paper, an adaptive artificial neural network model is developed in order to restore severely corrupted images. The proposed new and effective impulse noise reduction filter is named as adaptive neural network models with an algorithm based on artificial neural networks. Networks trained at different noise intensities get activated according to the intensity of the noise and estimate the most suitable neighboring pixel that can replace the corrupted pixel. The proposed algorithm reduces impulse noise effectively while also protecting the details. Experimental results show that the proposed algorithm performs better compared with other traditional filters.

Optimal estimation of the position of non-Gaussian impulse noise in images

An algorithm for optimal estimation (in the sense of the minimum average risk criterion) of the spatial position of non-Gaussian impulse noise in digital images is proposed based on the signal difference between the center of the slide aperture and its other elements. An expression for the boundary values of the admissible signal interval is obtained that provides the potentially achievable zero probability of noise estimation error. Results of numerical studies of the efficiency of the synthesized algorithm show its advantages over the well-known nonoptimal algorithm.

DETECTION AND REMOVAL OF IMPULSE NOISE IN DIGITAL IMAGES

DETECTION AND REMOVAL OF IMPULSE NOISE IN DIGITAL IMAGES

Noise-exclusive adaptive filtering framework for removing impulse noise in digital images

Noise-exclusive adaptive filtering framework for removing impulse noise in digital images

A noise-exclusive adaptive filtering framework for removing impulse noise in digital images

A class of noise-exclusive adaptive filters for removing impulse noise from digital images is developed and analyzed in this brief. The filtering scheme is based on noise detection using a self-organizing neural network and noise excluding estimation. These filters suppress impulse noise effectively while preserving fine image details. Applications of the filters to several images show that their properties of efficient impulse noise suppression, edges and fine detail preservation, minimum signal distortion, or minimum mean square error are better than those of the traditional median-type filters.

A neural network adaptive filter for the removal of impulse noise in digital images

A neural network adaptive filter is introduced for the removal of impulse noise in digital images. Using pixel classification by a self-organising neural network to detect the positions of the noisy pixels, the filter is able to effectively eliminate the impulses while retaining image integrity. Detailed statistical analysis and comparison of the proposed method with traditional median-type filters are provided. A visual example is given to demonstrate the performance of the proposed filter.