Problem of impulse noise reduction is a very well studied problem in image processing community and many different approaches have been proposed to tackle this problem. In the current work, the problem of fixed value impulse noise (salt and pepper) removal from images is investigated by use of a Markov Random Field (MRF) models with smoothness priors. After the formulation of the problem as an inpainting problem, graph cuts with $α$-expansion moves are considered for minimization of the energy functional. As for comparisons, several other minimization techniques that are widely used for MRF models' optimization are considered and the results are compared using Peak-Signal-to-Noise-Ratio (PSNR) and Structural Similarity Index (SSIM) as metrics. The investigations show the superiority of graph cuts with $α$-expansion moves over the other techniques both in terms of PSNR and also computational times.
China University of Geosciences（中国地质大学）；Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems（湖北省复杂系统先进控制与智能自动化重点实验室）
In this paper, a novel algorithm called a non-local adaptive mean filter (NAMF) for removing salt-and-pepper (SAP) noise from corrupted images is presented. We employ an efficient window detector with adaptive size to detect the noise, the noisy pixel will be replaced by the combination of its neighboring pixels, and finally we use a SAP noise based non-local mean filter to reconstruct the intensity values of noisy pixels. Extensive experimental results demonstrate that NAMF can obtain better performance in terms of quality for restoring images at all levels of SAP noise.
Liaoning Normal University（辽宁师范大学）；Jilin University（吉林大学）
In this paper, an image denoising algorithm is proposed for salt and pepper noise. First, a generative model is built on a patch as a basic unit and then the algorithm locates the image noise within that patch in order to better describe the patch and obtain better subsequent clustering. Second, the algorithm classifies patches using a generative clustering method, thus providing additional similarity information for noise repair and suppressing the interference of noise, abandoning those categories that consist of a smaller number of patches. Finally, the algorithm builds a non-local switchin…
University of Electronic Science and Technology of China（电子科技大学）；Chongqing College of Electronic Engineering（重庆电子工程职业学院）；Minnan Normal University（闽南师范大学）
Because of the limitations of the infrared imaging principle and the properties of infrared imaging systems, infrared images have some drawbacks, including a lack of details, indistinct edges, and a large amount of salt-andpepper noise. To improve the sparse characteristics of the image while maintaining the image edges and weakening staircase artifacts, this paper proposes a method that uses the Lp quasinorm instead of the L1 norm and for infrared image deblurring with an overlapping group sparse total variation method. The Lp quasinorm introduces another degree of freedom, better describes i…
The robust principal component analysis (RPCA), which aims to estimate underlying low-rank and sparse structures from the degraded observation data, has found wide applications in computer vision. It is usually replaced by the principal component pursuit (PCP) model in order to pursue the convex property, leading to the undesirable overshrink problem. In this paper, we propose a dual weighted lp-norm (DWLP) model with a more reasonable weighting rule and weaker powers, which greatly generalizes the previous work and provides a better approximation to the rank minimization problem for original …
Microsoft（微软）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Uber（优步）；University of Science and Technology of China（中国科学技术大学）；Hong Kong Polytechnic University（香港理工大学）
We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise. A median layer simply performs median filtering on all feature channels. By adding this kind of layer into some widely used fully convolutional deep neural networks, we develop an end-to-end network that removes the extremely high-level s&p noise without performing any non-trivial preprocessing tasks, which is different from all the existing literature in s&p noise removal. Experiments show that inserting median layers into a simp…
University of Electronic Science and Technology of China（电子科技大学）
There are several previous methods based on neural network can have great performance in denoising salt and pepper noise. However, those methods are based on a hypothesis that the value of salt and pepper noise is exactly 0 and 255. It is not true in the real world. The result of those methods deviate sharply when the value is different from 0 and 255. To overcome this weakness, our method aims at designing a convolutional neural network to detect the noise pixels in a wider range of value and then a filter is used to modify pixel value to 0, which is beneficial for further filtering. Addition…
Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）
Image denoising is one of the preliminary steps in image processing methods in which the presence of noise can deteriorate the image quality. To overcome this limitation, in this paper a improved two-stage fuzzy filter is proposed for filtering salt and pepper noise from the images. In the first-stage, the pixels in the image are categorized as good or noisy based on adaptive thresholding using type-2 fuzzy logic with exclusively two different membership functions in the filter window. In the second-stage, the noisy pixels are denoised using modified ordinary fuzzy logic in the respective filt…
University of Electronic Science and Technology（电子科技大学）；Minnan Normal University（闽南师范大学）
Salt and pepper noise removal is a common inverse problem in image processing. Traditional denoising methods have two limitations. First, noise characteristics are often not described accurately. For example, the noise location information is often ignored and the sparsity of the salt and pepper noise is often described by L1 norm, which cannot illustrate the sparse variables clearly. Second, conventional methods separate the contaminated image into a recovered image and a noise part, thus resulting in recovering an image with unsatisfied smooth parts and detail parts. In this study, we introd…
TIT College（TIT学院）；RGTU（拉吉夫·甘地技术大学）；Laxmipati Group of Institutions（拉克西米帕蒂教育集团）；RKDF College（RKDF学院）；IES college（IES学院）；LIST college（LIST学院）
When an image is formed, factors such as lighting (spectra, source, and intensity) and camera characteristics (sensor response, lenses) affect the appearance of the image. Therefore, the prime factor that reduces the quality of the image is noise. It hides the important details and information of images. In order to enhance the qualities of the image, the removal of noises become imperative and that should not at the cost of any loss of image information. Noise removal is one of the pre-processing stages of image processing. In this paper a new method for the enhancement of grayscale images is…
With the advent of recent advances in unsupervised learning, efficient training of a deep network for image denoising without pairs of noisy and clean images has become feasible. However, most current unsupervised denoising methods are built on the assumption of zero-mean noise under the signal-independent condition. This assumption causes blind denoising techniques to suffer brightness shifting problems on images that are greatly corrupted by extreme noise such as salt-and-pepper noise. Moreover, most blind denoising methods require a random masking scheme for training to ensure the invarianc…
Ernst & Young (EY) LLP（安永会计师事务所有限责任合伙）；EY GDS (CS) Poland Sp. z o.o（安永全球交付服务（中欧）波兰有限责任公司）
Removing noise from scanned pages is a vital step before their submission to the optical character recognition (OCR) system. Most available image denoising methods are supervised where the pairs of noisy/clean pages are required. However, this assumption is rarely met in real settings. Besides, there is no single model that can remove various noise types from documents. Here, we propose a unified end-to-end unsupervised deep learning model, for the first time, that can effectively remove multiple types of noise, including salt \& pepper noise, blurred and/or faded text, as well as watermarks f…
One image processing application that is very helpful for humans is to improve image quality, poor image quality makes the image more difficult to interpret because the information conveyed by the image is reduced. In the process of the acquisition of medical images, the resulting image has decreased quality (degraded) due to external factors and medical equipment used. For this reason, it is necessary to have an image processing process to improve the quality of medical images, so that later it is expected to help facilitate medical personnel in analyzing and translating medical images, which…
Convolutional Neural Network is good at image classification. However, it is found to be vulnerable to image quality degradation. Even a small amount of distortion such as noise or blur can severely hamper the performance of these CNN architectures. Most of the work in the literature strives to mitigate this problem simply by fine-tuning a pre-trained CNN on mutually exclusive or a union set of distorted training data. This iterative fine-tuning process with all known types of distortion is exhaustive and the network struggles to handle unseen distortions. In this work, we propose distortion r…
University of Burgundy（勃艮第大学）；Higher School of Technology（高等技术学院）；Faculty of Sciences, University of Mohammed V（穆罕默德五世大学理学院）
Due to the current progress in Internet, digital contents (video, audio and images) are widely used. Distribution of multimedia contents is now faster and it allows for easy unauthorized reproduction of information. Digital watermarking came up while trying to solve this problem. Its main idea is to embed a watermark into a host digital content without affecting its quality. Moreover, watermarking can be used in several applications such as authentication, copy control, indexation, Copyright protection, etc. In this paper, we propose a blind robust image watermarking approach as a solution to …
Image blurring refers to the degradation of an image wherein the image's overall sharpness decreases. Image blurring is caused by several factors. Additionally, during the image acquisition process, noise may get added to the image. Such a noisy and blurred image can be represented as the image resulting from the convolution of the original image with the associated point spread function, along with additive noise. However, the blurred image often contains inadequate information to uniquely determine the plausible original image. Based on the availability of blurring information, image deblurr…
University of Tübingen（蒂宾根大学）；Centre for Integrative Neuroscience, University of Tübingen（蒂宾根大学整合神经科学中心）；International Max Planck Research School for Intelligent Systems（国际马克斯·普朗克智能系统研究院）；Graduate School of Neural and Behavioural Sciences, University of Tübingen（蒂宾根大学神经与行为科学研究生院）；University of Potsdam（波茨坦大学）；Bernstein Center for Computational Neuroscience Tübingen（蒂宾根伯恩斯坦计算神经科学中心）；Max Planck Institute for Biological Cybernetics（马克斯·普朗克生物控制论研究所）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using three well known DNNs (ResNet-152, VGG-19, GoogLeNet) we find the human visual system to be more robust to nearly all of the tested image manipulations, and we observe progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker. Secondly, we show that DNNs trained directly on distorted images consistently surpass human performance on the exact distortion types they were trai…
Ranada Prasad Shaha University（拉纳达·普拉萨德·沙哈大学）；Khulna University of Engineering and Technology（库尔纳工程技术大学）
Human Identity verification has always been an eye-catching goal in digital based security system. Authentication or identification systems developed using human characteristics such as face, finger print, hand geometry, iris, and voice are denoted as biometric systems. Among the various characteristics, Iris recognition trusts on the idiosyncratic human iris patterns to find out and corroborate the identity of a person. The image is normally contemplated as a gathering of information. Existence of noises in the input or processed image effects degradation in the image superiority. It should b…
