Noisy Image Reconstruction Research Articles

Information Visualization Based on Visual Transmission and Multimedia Data Fusion

With the rapid development of information technology, the application media of visual identity design has been greatly broadened, and the requirements of dynamic variability and interactivity of vision have become higher and higher. The introduction of data as an element has brought new semantic endowments to visual design. To solve the problems of noisy image reconstruction and many outliers in traditional art design, this paper uses an improved phase correlation algorithm to reconstruct multimedia video images. In addition, based on the visual characteristics of human eyes, a multi-feature fusion viewpoint image quality evaluation algorithm is proposed. The simulation results show that the method adopted in this paper improves the uneven image texture in art design, and its real effect is greatly enhanced.

NIRN: Self-supervised noisy image reconstruction network for real-world image denoising

NIRN: Self-supervised noisy image reconstruction network for real-world image denoising

Remote Sensing Image Denoising Based on Deep and Shallow Feature Fusion and Attention Mechanism

Optical remote sensing images are widely used in the fields of feature recognition, scene semantic segmentation, and others. However, the quality of remote sensing images is degraded due to the influence of various noises, which seriously affects the practical use of remote sensing images. As remote sensing images have more complex texture features than ordinary images, this will lead to the previous denoising algorithm failing to achieve the desired result. Therefore, we propose a novel remote sensing image denoising network (RSIDNet) based on a deep learning approach, which mainly consists of a multi-scale feature extraction module (MFE), multiple local skip-connected enhanced attention blocks (ECA), a global feature fusion block (GFF), and a noisy image reconstruction block (NR). The combination of these modules greatly improves the model’s use of the extracted features and increases the model’s denoising capability. Extensive experiments on synthetic Gaussian noise datasets and real noise datasets have shown that RSIDNet achieves satisfactory results. RSIDNet can improve the loss of detail information in denoised images in traditional denoising methods, retaining more of the higher-frequency components, which can have performance improvements for subsequent image processing.

Adaptive gradient-based block compressive sensing with sparsity for noisy images

This paper develops a novel adaptive gradient-based block compressive sensing (AGbBCS_SP) methodology for noisy image compression and reconstruction. The AGbBCS_SP approach splits an image into blocks by maximizing their sparsity, and reconstructs images by solving a convex optimization problem. In block compressive sensing, the commonly used square block shapes cannot always produce the best results. The main contribution of our paper is to provide an adaptive method for block shape selection, improving noisy image reconstruction performance. The proposed algorithm can adaptively achieve better results by using the sparsity of pixels to adaptively select block shape. Experimental results with different image sets demonstrate that our AGbBCS_SP method is able to achieve better performance, in terms of peak signal to noise ratio (PSNR) and computational cost, than several classical algorithms.

Horizontal and Vertical Nuclear Norm-Based 2DLDA for Image Representation

2-D linear discriminant analysis (2DLDA) has been widely used in pattern recognition and image classification. 2DLDA selects discriminative features from the up and left corner of images. However, 2DLDA uses the Frobenius norm (F-norm), which is sensitive to noise or outliers in data, as a metric. In this paper, we propose a novel framework, called horizontal and vertical nuclear norm-based 2DLDA (HVNN-2DLDA) for image representation. In the proposed framework, HVNN-2DLDA methods (i.e., HNN-2DLDA and VNN-2DLDA) are proposed, and both use the nuclear norm as a criterion. The nuclear norm can provide more structure and global information for the reconstruction of noisy images. HNN-2DLDA and VNN-2DLDA represent images in the row and column directions, respectively. In addition, by combining the row and column directions, we propose a bilateral nuclear norm-based 2DLDA method called BNN-2DLDA. The advantage of BNN-2DLDA over HNN-2DLDA and VNN-2DLDA is that an image sample can be represented by both the row and the column directions instead of only the row or column direction. HVNN-2DLDA learns a set of local optimal projection vectors by maximizing the ratio of the nuclear norm of the between-class scatter matrix and the nuclear norm of the within-class scatter matrix. To verify the robustness and recognition performance in image classification of HVNN-2DLDA, six public image databases are used for experiments. The experimental results demonstrate the effectiveness and the feasibility of the proposed framework.

Reconstruction of noisy images via stochastic resonance in nematic liquid crystals

We employ nematic liquid crystals as the nonlinear medium to recover noisy images via stochastic resonance, in which nonlinear coupling allows signals to grow at the expense of noise. The process is theoretically analyzed and the cross-correlation is numerically calculated. It is found that the quality of output images is affected by the input noise intensity, the applied voltage and the correlation length of noise light. Noise-hidden images can be effectively recovered by optimizing these parameters. The results suggest that nematic liquid crystals can be used for reconstruction of noisy images via stochastic resonance based on modulation instability with molecule reorientation nonlinearity.

Noisy image reconstruction based on low-rank in UAV wireless transmission

Compressed sensing changes the conventional image processing model of full collection-sampling-compression-transmission-reconstruction and provides a more feasible way to the UAV wireless transmission. Existing matching pursuit algorithms cannot simultaneously meet the requirements of reconstruction accuracy and reconstruction efficiency in UAV wireless transmission, especially when the images are polluted by some noise. Hence, we propose an effective noisy image reconstruction algorithm based on low-rank which introduces the low-rank matrix decomposition and the Augmented Lagrange Multiplier to realize the tradeoff between reconstruction accuracy and reconstruction efficiency. Experimental results verify that the proposed LR algorithm has a superior and stable reconstruction performance on noisy image reconstruction compared with the matching pursuit algorithms.

Noisy Image Reconstruction Via Fast Linearized Lagrangian Dual Alternating Direction Method of Multipliers

In this paper, an efficient noisy image reconstruction algorithm based on compressed sensing in the wavelet domain is proposed. The new algorithm is composed of three steps. Firstly, the noisy imag...

Analysis of noise sensitivity of Tchebichef and Zernike moments with application to image watermarking

In this paper we show that, contrary to the common belief found in some of the literature, Tchebichef moments are more sensitive to image additive noise than Zernike moments. We examine the problem of noisy image reconstruction by the method of orthogonal moments. We comparatively show this by imposing different types and levels of noise on various images and by measuring the error due to the added noise alone after image reconstruction. Here the error due to the added noise alone is defined, quantified and calculated by subtracting the noise-free image reconstruction error from the total noisy reconstructed error, which includes both reconstruction and added noise errors. The reconstruction error is with respect to a given original non-noisy image. A reconstruction measure metric for better evaluating the sensitivity of orthogonal moments towards noise added to images, namely accumulative relative error, is also introduced and proposed. As a result of this noise analysis study, we also present an empirical comparative study of Tchebichef and Zernike moments in image watermarking applications. In particular, we consider the case of moment-based watermarking schemes involving moment watermarks being embedded in a given carrier image moments. We show that the Tchebichef moments of a given image are more sensitive to image malicious and intended manipulations than Zernike moments, and hence are more capable of detecting tampering performed on watermarked images during transmission. However, this suggests and will most likely make Zernike orthogonal moments more suitable as moment descriptors employed as pattern features in scene registration, recognition, modeling, and data compression in noisy scenes, than Tchebitchef moments.

Methods of statistical spatial filtering of images on the basis of a local group of transformations

New methods for processing images that are based on statistical estimates of the invariants of the groups of automorphism of images are considered. A group-theoretical statistical approach to the solution of image-reconstruction problems, which allows visualization of the structural and functional connections in a tested object, was developed. The proposed methods are applied to the solution of problems of the reconstruction of noisy images in nondestructive testing.

Storage and recall capabilities of fuzzy morphological associative memories with adjunction-based learning

We recently employed concepts of mathematical morphology to introduce fuzzy morphological associative memories (FMAMs), a broad class of fuzzy associative memories (FAMs). We observed that many well-known FAM models can be classified as belonging to the class of FMAMs. Moreover, we developed a general learning strategy for FMAMs using the concept of adjunction of mathematical morphology. In this paper, we describe the properties of FMAMs with adjunction-based learning. In particular, we characterize the recall phase of these models. Furthermore, we prove several theorems concerning the storage capacity, noise tolerance, fixed points, and convergence of auto-associative FMAMs. These theorems are corroborated by experimental results concerning the reconstruction of noisy images. Finally, we successfully employ FMAMs with adjunction-based learning in order to implement fuzzy rule-based systems in an application to a time-series prediction problem in industry.

On Kernel Selection of Multivariate Local Polynomial Modelling and its Application to Image Smoothing and Reconstruction

This paper studies the problem of adaptive kernel selection for multivariate local polynomial regression (LPR) and its application to smoothing and reconstruction of noisy images. In multivariate LPR, the multidimensional signals are modeled locally by a polynomial using least-squares (LS) criterion with a kernel controlled by a certain bandwidth matrix. Based on the traditional intersection confidence intervals (ICI) method, a new refined ICI (RICI) adaptive scale selector for symmetric kernel is developed to achieve a better bias-variance tradeoff. The method is further extended to steering kernel with local orientation to adapt better to local characteristics of multidimensional signals. The resulting multivariate LPR method called the steering-kernel-based LPR with refined ICI method (SK-LPR-RICI) is applied to the smoothing and reconstruction problems in noisy images. Simulation results show that the proposed SK-LPR-RICI method has a better PSNR and visual performance than conventional LPR-based methods in image processing.

Numerical piecewise-uniform regularization for two-dimensional ill-posed problems

The problem of piecewise-uniform regularization for two-dimensional ill-posed problems with bounded discontinuous solutions is under discussion. The algorithm of modified Tikhonov regularization is applied to solve the problem on the class of functions of two variables with bounded VH-variation as previously proposed by the author. In finite-dimensional form, the algorithm is reduced to mathematical programming with a nonsmooth target function. After smooth approximation, the algorithm is numerically implemented in an effective way to ensure the piecewise-uniform convergence of the approximate solutions to the exact solution of an ill-posed problem. The quality of obtained piecewise-uniform regularization is illustrated by numerical experiments in blurred and noisy image reconstruction.