Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior

The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio (SNR) of the measurements. The decreased SNR reduces the reliability of measured features or information extracted from HSIs, thus calling for effective denoising techniques. This work aims to estimate clean HSIs from observations corrupted by mixed noise (containing Gaussian noise, impulse noise, and dead-lines/stripes) by exploiting two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain. We take advantage of the spectral low-rankness of HSIs by representing spectral vectors in an orthogonal subspace, which is learned from observed images by a new method. Subspace representation coefficients of HSIs are learned by solving an optimization problem plugged with an image prior extracted from a neural denoising network. The proposed method is evaluated on simulated and real HSIs. An exhaustive array of experiments and comparisons with state-of-the-art denoisers were carried out.

Methods for Preventing Visual Attacks in Convolutional Neural Networks Based on Data Discard and Dimensionality Reduction

The article is devoted to the study of convolutional neural network inference in the task of image processing under the influence of visual attacks. Attacks of four different types were considered: simple, involving the addition of white Gaussian noise, impulse action on one pixel of an image, and attacks that change brightness values within a rectangular area. MNIST and Kaggle dogs vs. cats datasets were chosen. Recognition characteristics were obtained for the accuracy, depending on the number of images subjected to attacks and the types of attacks used in the training. The study was based on well-known convolutional neural network architectures used in pattern recognition tasks, such as VGG-16 and Inception_v3. The dependencies of the recognition accuracy on the parameters of visual attacks were obtained. Original methods were proposed to prevent visual attacks. Such methods are based on the selection of “incomprehensible” classes for the recognizer, and their subsequent correction based on neural network inference with reduced image sizes. As a result of applying these methods, gains in the accuracy metric by a factor of 1.3 were obtained after iteration by discarding incomprehensible images, and reducing the amount of uncertainty by 4–5% after iteration by applying the integration of the results of image analyses in reduced dimensions.

Advance in Image and Audio Restoration and their Assessments: A Review

Image restoration is the process of restoring the original image from a degraded one. Images can be affected by various types of noise, such as Gaussian noise, impulse noise, and affected by blurring, which is happened during image recordings like motion blur, Out-of-Focus Blur, and others. Image restoration techniques are used to reverse the effect of noise and blurring. Restoration of distorted images can be done using some information about noise and the blurring nature or without any knowledge about the image degradation process. Researchers have proposed many algorithms in this regard; in this paper, different noise and degradation models and restoration methods will be discussed and review some researches in this field.

Identification of Data Injection Attacks in Networked Control Systems Using Noise Impulse Integration

The benefits of using Networked Control Systems (NCS) in the growing Industry 4.0 arenumerous, including better management and operational capabilities, as well as costs reduction.However, despite these benefits, the use of NCSs can also expose physical plants to new threatsoriginated in the cyber domain—such as data injection attacks in NCS links through which sensorsand controllers transmit signals. In this sense, this work proposes a link monitoring strategy toidentify linear time-invariant (LTI) functions executed during controlled data injection attacksby a Man-in-the-Middle hosted in an NCS link. The countermeasure is based on a bioinspiredmetaheuristic, called Backtracking Search Optimization Algorithm (BSA), and uses white Gaussiannoise to excite the attack function. To increase the accuracy of this countermeasure, it is proposedthe Noise Impulse Integration (NII) technique, which is developed using the radar pulse integrationtechnique as inspiration. The results demonstrate that the proposed countermeasure is able toaccurately identify LTI attack functions, here executed to impair measurements transmitted bythe plant sensor, without interfering with the NCS behavior when the system is in its normaloperation. Moreover, the results indicate that the NII technique can increase the accuracy of the attackidentification.

Hyperspectral Image Denoising via Subspace Low-rank Representation and Spatial‐spectral Total Variation

Hyperspectral images (HSIs) acquired actually often contain various types of noise, such as Gaussian noise, impulse noise, and dead lines. On the basis of land covers, the spectral vectors in HSI can be separated into different classifications, which means the spectral space can be regarded as a union of several low-rank (LR) subspaces rather than a single LR subspace. Recently, LR constraint has been widely applied for denoising HSI. However, those LR-based methods do not constrain the intrinsic structure of spectral space. And these methods cannot make better use of the spatial or spectral features in an HSI cube. In this article, a framework named subspace low-rank representation combined with spatial‐spectral total variation regularization (SLRR-SSTV) is proposed for HSI denoising, where the SLRR is introduced to more precisely satisfy the low-rank property of spectral space, and the SSTV regularization is involved for the spatial and spectral smoothness enhancement. An inexact augmented Lagrange multiplier method by alternative iteration is employed for the SLRR-SSTV model solution. Both simulated and real HSI experiment results demonstrate that the proposed method can achieve a state-of-the-art performance in HSI denoising.

Examination of Skin Cancer Images using Wavelet De-noising

The objective of this paper is de-noising of melanoma images using wavelets because, dermatoscopy images are corrupted by noise, which leads to fault diagnosis. Hence de-noising is essential in melanoma skin cancer image to remove the salt and pepper noise(impulse noise) by preserving the melanoma image original information. The wavelet thresholding techniques are used in this paper to de-noise the melanoma image and improved the quality of an image. Wavelet de-noising algorithm has been developed employing soft and hard thresholding techniques. It works on Daubechies, Symlet, biorthogonal wavelets at decomposition level5. Image objective performance metrics like peak signal to noise ratio, mean square error and statistical performance metrics like mean, median, standard deviation, L1 norm, L2 norm are observed and analyzed for melanoma images

Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.

An efficient Scheme to Remove Low Density Impulse Noise From A Digital Image

An improved adaptive noise reduction scheme for images that are highly corrupted by Salt-and-Pepper noise(impulse noise), is proposed in this paper which efficiently removes the salt and pepper noise while preserving the details. The proposed scheme efficiently identifies and reduces salt and pepper noise. The algorithm utilizes an IIR filter with controlled parameters to get better image quality than the existing methods of noise removing. A comparative analysis between the proposed scheme and other techniques of noise reduction or removing is presented in order to show the advantages of the proposed scheme in removing the noisy pixels and producing a better PSNR.

Experimental Measurements of Effects of Occupancy Class on Reverberation Time

Experimental measurements have been focused on the issue of change in reverberation time T20 and T30 (s) in a selected room (classroom) due to its occupancy by persons (students) and changes in reverberation time T20 and T30 (s) as a result of the excitation signal during measurement. Reverberation time was measured in a standard room at the Faculty of Civil Engineering of the Slovak Technical University in Bratislava (classroom B318) by the use of NORSONIC (NORSONIC NOR 280 amplifier, an omnidirectional sound source - NORSONIC NOR 270, NORSONIC TYPE 118, and microphone and preamplifier). The room (classroom B318) was selected as a model classroom in terms of its dimensions, i.e. volume (225 m3), arrangement of furniture, and maximum number of students during standard lectures (up to 24 people). The main task of this study was the inter-comparison of measurement results of reverberation times T20 and T30 (s), with regard to room occupancy of students (100%, 50%, 0%), the position of students (sitting and standing), and the excitation signal (pink noise, white noise, impulse - bursting of an inflated balloon).