A fast adaptive and selective mean filter is presented to remove salt and pepper noise effectively from images corrupted with higher noise densities. The algorithm achieves better results in terms of visual quality and in terms of peak signal-to-noise ratio, mean absolute error, mean structural similarity index measure, image enhancement factor, and edge preservation ratio than many existing state-of-the-art algorithms at all noise densities. Adaptive filters that use variable window size produce better restoration of salt and pepper noise at higher noise densities than filters that use fixed window size, but they consume more time. This makes them practically impossible to implement them in digital image acquisition devices. Hence, reducing the execution time of adaptive filters is vital. The proposed algorithm consumes around 90% less time for lower noise densities and 50% less time for higher noise densities than the adaptive weighted mean filter, one of the best available adaptive filters in the literature for high-density salt and pepper noise removal.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2017.0199

Fast adaptive and selective mean filter for the removal of high-density salt and pepper noise

Image denoising is a common problem during image processing. Salt and pepper noise may contaminate an image by randomly converting some pixel values into 255 or 0. The traditional image denoising algorithm is based on filter design or interpolation algorithm. There exists no work using the convolutional neural network (CNN) to directly remove salt and pepper noise to the authors’ knowledge. In this study, they utilise CNN with the multi-layer structure for the removal of salt and pepper noise, which contains padding, batch normalisation and rectified linear unit. In training, they divide images into three parts: training set, validation set and test set. Experimental results demonstrate that the architecture can effectively remove salt and pepper noise for the various noisy images. In addition, their model can remove high-density noise well due to the extensive local receptive fields of the deep neural networks. Finally, extensive experimental results show that their denoiser is effective for those images with a large number of interference pixels which may cause misjudgement. In a word, they generalise the application of CNN to salt and pepper noise removal and obtain competitive results.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2018.6004

Deep CNN for removal of salt and pepper noise

Image restoration is an important and interesting problem in the field of image processing because it improves the quality of input images, which facilitates postprocessing tasks. The salt-and-pepper noise has a simpler structure than other noises, such as Gaussian and Poisson noises, but is a very common type of noise caused by many electronic devices. In this article, we propose a two-stage filter to remove high-density salt-and-pepper noise on images. The range of application of the proposed denoising method goes from low-density to high-density corrupted images. In the experiments, we assessed the image quality after denoising using the peak signal-to-noise ratio and structural similarity metric. We also compared our method against other similar state-of-the-art denoising methods to prove its effectiveness for salt and pepper noise removal. From the findings, one can conclude that the proposed method can successfully remove super-high-density noise with noise level above 90%.

/pdf/a-two-stage-filter-for-high-density-salt-and-pepper-4jd0koci1v.pdf

A Two-Stage Filter for High Density Salt and Pepper Denoising

The paper presents a novel adaptive trimmed median (ATM) filter to remove salt-and-pepper (SAP) noise of high noise density (ND). The proposed filter computes median of trimmed window of adaptive size containing noise-free pixels (NFP) for ND up medium range while performs new interpolation-based procedure at high ND. Further, for the rare scenarios especially at the boundary where denoising using interpolation is not good enough, the proposed filter denoises the candidate pixel with the help of nearest processed pixels. The proposed ATM filter effectively suppresses SAP noise because denoising mostly utilizes original non-noisy pixels. The proposed algorithm is evaluated for varying ND (10–90%) with different benchmark images (greyscale and coloured) over the existing approaches. The proposed ATM filter on an average provides 1.59 dB and 0.37 dB higher PSNR values on the greyscale and color images, respectively.

Restoration of highly salt-and-pepper-noise-corrupted images using novel adaptive trimmed median filter

This study presents a Convolutional Neural Network (CNN) model to effectively recognize the presence of Gaussian noise and its level in images. The existing denoising approaches are mostly based on an assumption that the images to be processed are corrupted with noises. This work, on the other hand, aims to intelligently evaluate if an image is corrupted, and to which level it is degraded, before applying denoising algorithms. We used 12000 and 3000 standard test images for training and testing purposes, respectively. Different noise levels are introduced to these images. The overall accuracy of 74.7% in classifying 10 classes of noise levels are obtained. Our experiments and results have proven that this model is capable of performing Gaussian noise detection and its noise level classification.

Detection of Gaussian noise and its level using deep convolutional neural network

This study presents a novel method for automatic detection and removal of high-density impulse noises. The method consists of two parts: the impulse detection part and the impulse noise removal part. In impulse detection part, an automatic detector based on local mean and variance (LMVD) is presented, which can automatically pick out noisy image from massive images and output corrupted grey levels. The detector utilises LMVD of the neighbourhood of corrupted pixels to simulate the cognitive processes of human observing noisy image. In impulse noise removal part, the Newton–Thiele filter (NTF) instead of median filter is applied to remove impulse noise. The process to construct NTF can be divided into two steps: setting up the grid and constructing the Newton–Thiele's rational interpolation on the grid. First, eight adjacent pixels of the corrupted centre pixel are used to construct the two-dimensional grid. If a pixel in the grid is corrupted, a four-direction linear interpolation algorithm will be performed to provide a rough estimate to the corrupted pixel. Second, the corrupted centre pixel value will be updated by Newton–Thiele's rational interpolation on the grid. The NTF has better robustness than existing filters because it does not need to adjust window size or other parameters. Simulations reveal that the proposed detector and filter have perfect performance in terms of both quantitative evaluation and visual quality, especially it can remove the impulse noise effectively even at 90% noise level.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2014.0286

Jieqing Tan

Papers

Deep CNN for removal of salt and pepper noise

Automatic detection and removal of high-density impulse noises