Image Restoration Research Articles

Influence of Ethical PR Practices on Corporate Image during Scandals in Tanzania

Purpose: The aim of the study was to assess the influence of ethical PR practices on corporate image during scandals in Tanzania. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study indicated that maintaining ethical standards significantly mitigates reputational damage. Ethical PR strategies, including transparency, accountability, and timely communication, help companies navigate crises more effectively. Transparency ensures that all relevant information is disclosed promptly, fostering trust among stakeholders. Accountability demonstrates a company's commitment to taking responsibility for its actions, which can help regain public trust. Timely communication ensures that the company controls the narrative, reducing the spread of misinformation. Studies indicate that companies employing ethical PR practices during scandals tend to recover faster and sustain less long-term damage to their corporate image. Additionally, these practices can turn a crisis into an opportunity to showcase the company's integrity and commitment to ethical standards, potentially strengthening its reputation in the long run. Overall, ethical PR practices are crucial in safeguarding and enhancing corporate image during and after scandals. Implications to Theory, Practice and Policy: Situational crisis communication theory (SCCT), stakeholder theory and image restoration theory may be used to anchor future studies on assessing influence of ethical PR practices on corporate image during scandals in Tanzania. Encourage organizations to establish proactive ethical guidelines for PR professionals. Advocate for the establishment of a regulatory framework that mandates ethical practices in PR. This policy can help standardize ethical behavior across the industry, enhancing corporate accountability.

Method of 3D reconstruction of underwater concrete by laser line scanning

High-precision 3D (three-dimensional) reconstruction of underwater concrete is helpful for the detection and analysis of underwater concrete diseases. A prevalent method involves mounting optical cameras in waterproof housings to enable the 3D reconstruction of underwater concrete. Achieving millimetric accuracy in underwater 3D reconstruction poses a significant challenge in current research. The refraction of light on surfaces of different media and its reflection underwater complicate the process. In this paper, a 3D reconstruction method of underwater concrete based on laser line scanning is proposed. Firstly, the underwater imaging model is established by the ray tracing method, and the refraction conversion matrix is derived. Then, based on this matrix, the conversion relationship between the measured 3D size and the real 3D size of the underwater object is obtained. Finally, image restoration technology is introduced in the process of laser stripe extraction to eliminate the influence of reflected light on the extraction of laser stripe center, to improve the reconstruction accuracy. We used the independently developed laser line 3D scanning equipment to carry out experimental analysis. The results showed that the error rate caused by direct reconstruction of underwater objects was 9.75 %. Additionally, the error rate obtained from 3D reconstruction of underwater objects using the conversion model was 2.89 %. This study provides technical support for the 3D detection of underwater concrete.

An Image Restoration Method for Improving Matching Robustness of Indoor Smoke Scene

An Image Restoration Method for Improving Matching Robustness of Indoor Smoke Scene

An optimal Bayesian strategy for comparing Wiener–Hunt deconvolution models in the absence of ground truth

Abstract This paper considers the quantitative comparison of several alternative models to perform deconvolution in situations where there is no ground truth data available. With applications to very large data sets in mind, we focus on linear deconvolution models based on a Wiener filter. Although comparatively simple, such models are widely prevalent in large scale setting such as high-resolution image restoration because they provide an excellent trade-off between accuracy and computational effort. However, in order to deliver accurate solutions, the models need to be properly calibrated in order to capture the covariance structure of the unknown quantity of interest and of the measurement error. This calibration often requires onerous controlled experiments and extensive expert supervision, as well as regular recalibration procedures. This paper adopts an unsupervised Bayesian statistical approach to model assessment that allows comparing alternative models by using only the observed data, without the need for ground truth data or controlled experiments. Accordingly, the models are quantitatively compared based on their posterior probabilities given the data, which are derived from the marginal likelihoods or evidences of the models. The computation of these evidences is highly non-trivial and this paper consider three different strategies to address this difficulty—a Chib approach, Laplace approximations, and a truncated harmonic expectation—all of which efficiently implemented by using a Gibbs sampling algorithm specialised for this class of models. In addition to enabling unsupervised model selection, the output of the Gibbs sampler can also be used to automatically estimate unknown model parameters such as the variance of the measurement error and the power of the unknown quantity of interest. The proposed strategies are demonstrated on a range of image deconvolution problems, where they are used to compare different modelling choices for the instrument’s point spread function and covariance matrices for the unknown image and for the measurement error.

Restoration of motion-blurred numeral image using a complex-amplitude diffractive processor.

We propose a complex-amplitude diffractive processor based on diffractive deep neural networks (D2NNs). By precisely controlling the propagation of an optical field, it can effectively remove the motion blur in numeral images and realize the restoration. Comparative analysis of phase-only, amplitude-only, and complex-amplitude diffractive processor reveals that the complex-amplitude network significantly enhances the performance of the processor and improves the peak signal-to-noise ratio (PSNR) of the images. Appropriate use of complex-amplitude networks contributes to reduce the number of network layers and alleviates alignment difficulties. Due to its fast processing speed and low power consumption, complex-amplitude diffractive processors hold potential applications in various fields including road monitoring, sports photography, satellite imaging, and medical diagnostics.

A diverse underwater image formation model for underwater image restoration

A diverse underwater image formation model for underwater image restoration

An accurate measurement method of irregular coal gangue volume based on adaptive image restoration and error compensation

To improve the accuracy of volume measurement for irregularly shaped coal gangue, defects in the depth image and various errors in volume measurement are analyzed. The gray difference similarity (Gds) is proposed for pre-classifying defects, and the depth and RGB information are used interactively for adaptive repair. To minimize the impact of abnormal gray distribution and irrelevant information on the repair process, the areas to be repaired are divided into abnormal regions or layers based on gray and distance weights. Taking into account parameters like geometric morphology (GeMo), volume (Ve), and GeMo × Ve, the influence of wrong imaging and gaps is fully considered. The reverse compensation of errors is achieved by applying the principles of mathematical statistics and the nonlinear surface fitting algorithm. The results show that compared with other algorithms, the proposed algorithm has the smallest measurement error of 6.614 % and is not easily affected by external factors.

A novel single-stage network for accurate image restoration

A novel single-stage network for accurate image restoration

An IDFPM-based algorithm without Lipschitz continuity to constrained nonlinear equations for sparse signal and blurred image restoration problems

The derivative-free projection method (DFPM) is widely used to solve constrained nonlinear equations. To guarantee the convergence of the derivative-free projection method, the mapping should be Lipschitz continuous, which is a strict requirement in theory. Hence, it is interesting to design the new DFPM that possesses nice convergence under weaker theoretical hypothesis. In this paper, we propose an inertial DFPM-based algorithm (named IDFPM), in which the inertial extrapolation step is embedded in the design for the search direction. The global convergence of the proposed algorithm is obtained without the Lipschitz continuity of the mapping. Numerical experiments are carried out for two kinds of problems. The one consists of eight test problems from classical literature and the compressed sensing model. The numerical results show that the proposed algorithm is promising.

Deeper, Sharper, Faster: Application of Efficient Transformer to Galaxy Image Restoration

The Transformer architecture has revolutionized the field of deep learning over the past several years in diverse areas, including natural language processing, code generation, image recognition, and time-series forecasting. We propose to apply Zamir et al.'s efficient transformer to perform deconvolution and denoising to enhance astronomical images. We conducted experiments using pairs of high-quality images and their degraded versions, and our deep learning model demonstrates exceptional restoration of photometric, structural, and morphological information. When compared with the ground-truth James Webb Space Telescope images, the enhanced versions of our Hubble Space Telescope–quality images reduce the scatter of isophotal photometry, Sérsic index, and half-light radius by factors of 4.4, 3.6, and 4.7, respectively, with Pearson correlation coefficients approaching unity. The performance is observed to degrade when input images exhibit correlated noise, point-like sources, and artifacts. We anticipate that this deep learning model will prove valuable for a number of scientific applications, including precision photometry, morphological analysis, and shear calibration.

Image restoration for digital line drawings using line masks

The restoration of digital images holds practical significance due to the fact that degradation of digital image data on the internet is common. State-of-the-art image restoration methods usually employ end-to-end trained networks. However, we argue that a network trained with diverse image pairs is not optimal for restoring line drawings which have extensive plain backgrounds. We propose a line-drawing restoration framework which takes a restoration neural network as backbone and processes an input degraded line drawing in two steps. First, a proposed mask-predicting network predicts a line mask which indicates the possible location of foreground and background in the potential original line drawing. Next, we feed the degraded input line drawing together with the predicted line mask into the backbone restoration network. The traditional L1 loss for the backbone restoration network is substituted with a masked Mean Square Error (MSE) loss. We test our framework on two classical image restoration tasks: JPEG restoration and super-resolution, and experiments demonstrate that our framework can achieve better quantitative and visual results in most cases.

Rethinking of Underwater Image Restoration Based on Circularly Polarized Light

Polarimetric imaging technology plays a crucial role in de-scattering, particularly in the field of underwater image restoration. Circularly polarized light (or the underlying circular polarization memory effect) has been proven to better preserve the polarization characteristics of detected light. Utilizing circularly polarized light as illumination can further enhance the effectiveness of polarization de-scattering techniques. After rethinking the advantages of circularly polarized light, this paper proposes a new method for underwater polarimetric imaging restoration that leverages the pre-processing of polarized sub-images and the correlation of polarization characteristics (i.e., the angle of polarization and degree of polarization). Additionally, to address the challenge of selecting scattering light parameters due to uneven light fields in target scenes, an intensity adjustment factor search algorithm is designed. This algorithm eliminates the need for the manual selection of scattering light parameters, effectively solving the problem of uneven illumination in restoration results. A series of experiments demonstrate that, compared to traditional algorithms, the proposed method offers superior detail restoration and higher robustness.

AI-POWERED IMAGE ENHANCEMENT IN FORENSIC APPLICATIONS: CHALLENGES AND OPPORTUNITIES

Recent progress in machine learning (ML) and computer vision has markedly enhanced the performance of automated facial recognition systems. Nonetheless, forensic applications frequently face issues with poor-quality and low-resolution images that pose challenges even to the most advanced facial recognition technologies. This research explores the potential of neural-based image enhancement and restoration techniques to recover degraded images while maintaining original correspondences for legal use. We evaluate super-resolution and deconvolution methods across two comprehensive and varied facial datasets. Our study employs twelve distinct GAN- and diffusion-based enhancement techniques. Our results offer insights and recommendations for the effective application of image enhancement in forensic facial recognition, highlighting the advantages and potential limitations of these advanced technologies.

Wearable damaged clothing fabric image recognition system based on image restoration algorithm

With the improvement of people's living standards and the rapid growth of national technological strength, wearable devices have gradually integrated into people's daily lives, making them convenient for people's work and lives. Traditional fabric detection methods often rely on manual visual inspection, which has low efficiency and accuracy that does not meet the requirements. In view of this, the research has innovatively integrated GAN, CNN, field Programmable gate array (FPGA), multi-stage image recovery algorithm based on graph neural network, for high frequency and low frequency fusion, and combined it with wearable devices to develop a new wearable fabric recovery and recognition system. The results showed that in the COCO dataset, after 250 iterations, the WFR2S algorithm achieved a damage identification accuracy of 95.5 %, a mean absolute error of 0.011, a real-time processing speed of 30 fps, and a resource consumption of 2.4 W. These data demonstrate the significant performance advantages of WFR2S in image recovery and damage detection. The newly developed image recovery algorithm improves the accuracy, availability and stability of damage recognition in garment fabrics. This study not only has theoretical value but also has broad application prospects in practical application. The aim is to contribute to the intelligent development of the garment industry.

Hyperspectral image restoration using noise gradient and dual priors under mixed noise conditions

AbstractImages obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic spectrum. However, due to sensor limitations and imperfections during the image acquisition and transmission phases, noise is introduced into the acquired image, which can have a negative impact on downstream analyses such as classification, target tracking, and spectral unmixing. Noise in hyperspectral images (HSI) is modelled as a combination from several sources, including Gaussian/impulse noise, stripes, and deadlines. An HSI restoration method for such a mixed noise model is proposed. First, a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian‐impulse noise by estimating both the clean data as well as the sparse/impulse noise levels. Second, a hyper‐Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image gradients. Third, to model the sparse nature of impulse noise, an ℓ1 − norm over the impulse noise gradient is used. Because the proposed methodology employs two distinct priors, the authors refer to it as the hyperspectral dual prior (HySpDualP) denoiser. To the best of authors' knowledge, this joint optimisation framework is the first attempt in this direction. To handle the non‐smooth and non‐convex nature of the general ℓp − norm‐based regularisation term, a generalised shrinkage/thresholding (GST) solver is employed. Finally, an efficient split‐Bregman approach is used to solve the resulting optimisation problem. Experimental results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’ proposed model outperforms state‐of‐the‐art methods, both visually and in terms of various image quality assessment metrics.

Alternative Arnoldi process for ill-conditioned tensor equations with application to image restoration

Alternative Arnoldi process for ill-conditioned tensor equations with application to image restoration

Mixed overlapping group sparse and nonconvex fractional-order image restoration algorithm

Mixed overlapping group sparse and nonconvex fractional-order image restoration algorithm

Multi-Scale and Multi-Layer Lattice Transformer for Underwater Image Enhancement

Underwater images are often subject to color deviation and a loss of detail due to the absorption and scattering of light. The challenge of enhancing underwater images is compounded by variations in wavelength and distance attenuation, as well as color deviation that exist across different scales and layers, resulting in different degrees of color deviation, attenuation and blurring. To address these issues, we propose a novel multi-scale and multi-layer lattice transformer (MMLattFormer) to effectively eliminate artifacts and color deviation, prevent over-enhancement, and preserve details across various scales and layers, thereby achieving more accurate and natural results in underwater image enhancement. The proposed MMLattFormer model integrates the advantage of LattFormer to enhance global perception with the advantage of “multi-scale and multi-layer”-configuration to leverages the differences and complementarities between features of various scales and layers to boost local perception. The proposed MMLattFormer model is comprised of multi-scale and multi-layer LattFormers. Each LattFormer primarily encompasses two modules: Multi-head Transposed-attention Residual Network (MTRN) and Gated-attention Residual Network (GRN). The MTRN module enables cross-pixel interaction and pixel-level aggregation in an efficient manner to extract more significant and distinguishable features, whereas the GRN module can effectively suppress under-informed or redundant features and retain only useful information, enabling excellent image restoration exploiting the local and global structures of the images. Moreover, to better capture local details, we introduce depth-wise convolution in these two modules before generating global attention maps and decomposing images into different features to better capture the local context in image features. The qualitative and quantitative results indicate that the proposed method outperforms state-of-the-art approaches in delivering more natural results. This is evident in its superior detail preservation, effective prevention of over-enhancement, and successful removal of artifacts and color deviation on several public datasets.

Gaussian second derivative blur kernels for image deblurring

Image blur is a common image degradation phenomenon that occurs in case of camera movement and lens defocusing. In order to deblur the image, point spread function (PSF) model is often used to match the real blur kernel, but currently the matching effect is poor. In this paper, an image restoration technique based on the Gaussian Second Derivative (GSD) fitted PSF is proposed to address this problem. Firstly, a GSD model is proposed. The actual image Line spread function (LSF) extracted by knife-edge method is modeled as a linear combination of a series of Gaussian second derivatives. Then, an improved annihilation filtering algorithm is proposed. The algorithm is used to estimate the parameters of the GSD model, so as to remove the errors in the actual complex scenarios. Finally, the image PSF is obtained by using the LSF curve fitted and corrected by the GSD model, and combined with the existing Richardson-Lucy (R-L) algorithm, the clear image is finally restored. Experimental results show that compared with the existing image restoration strategies based on other PSF models, this method can effectively improve the image restoration accuracy.

Multi-Layer Feature Restoration and Projection Model for Unsupervised Anomaly Detection

The anomaly detection of products is a classical problem in the field of computer vision. Image reconstruction-based methods have shown promising results in the field of abnormality detection. Most of the existing methods use convolutional neural networks to build encoding–decoding structures to do image restoration. However, the limited receptive field of convolutional neural networks makes the information considered in the image restoration process limited, and the downsampling in the encoder causes information loss, which is not conducive to performing fine-grained restoration of images. To solve this problem, we propose a multi-layer feature restoration and projection model (MLFRP), which enables the restoration process to be carried out on multi-scale feature maps through a block-level feature restoration module that fully considers the detail information and semantic information required for the restoration process. We conducted in-depth experiments on the MvtecAD anomaly detection benchmark dataset, which showed that our model outperforms current state-of-the-art anomaly detection methods.