Digital Image Restoration Research Articles

Inverse Techniques for Efficient Corneal Image Restoration

This paper presents two proposed approaches for digital restoration of corneal images. The first algorithm is based on Wiener Restoration approach. The second algorithm depends on regularized image restoration. As corneal images are usually acquired with confocal microscopes. Hence if the corneal layer is outside the focus of the microscopes, the image will be blurred. To solve this problem, the restoration process can be applied on the corneal image. Both Linear Minimum Mean Square Error (LMMSE) and regularized restoration are implemented. The evaluation metrics used to test the performance of the proposed restoration approaches are mean square error (MSE), peak signal to noise ratio (PSNR) and correlation coefficient. Simulations results reveal good success in restoration of corneal images refer to the mentioned evaluation metrics and appearance view.

RETRACTED: Digital object restoration using generalized regression neural network deep learning—Taking Dunhuang mural restoration as an example

In this research, Dunhuang murals is taken as the object of restoration, and the role of digital repair combined with deep learning algorithm in mural restoration is explored. First, the image restoration technology is described, as well as its advantages and disadvantages are analyzed. Second, the deep learning algorithm based on artificial neural network is described and analyzed. Finally, the deep learning algorithm is integrated into the digital repair technology, and a mural restoration method based on the generalized regression neural network is proposed. The morphological expansion method and anisotropic diffusion method are used to preprocess the image. The MATLAB software is used for the simulation analysis and evaluation of the image restoration effect. The results show that in the restoration of the original image, the accuracy of the digital image restoration technology is not high. The nontexture restoration technology is not applicable in the repair of large-scale texture areas. The predicted value of the mural restoration effect based on the generalized neural network is closer to the true value. The anisotropic diffusion method has a significant effect on the processing of image noise. In the image similarity rate, the different number of training samples and smoothing parameters are compared and analyzed. It is found that when the value of δ is small, the number of training samples should be increased to improve the accuracy of the prediction value. If the number of training samples is small, a larger value of δ is needed to get a better prediction effect, and the best restoration effect is obtained for the restored image. Through this study, it is found that this study has a good effect on the restoration model of Dunhuang murals. It provides experimental reference for the restoration of later murals.

Application of Digital Processing in Relic Image Restoration Design

Cultural relic is the carrier of human historic culture, which can reflect the cultural and social environment, but cultural relics as a material will be damaged over time. Before the advent of computer technology, the damaged cultural relics would not be repaired due to cost. Computer vision technology has been applied to the restoration of cultural relics, mainly for the virtual restoration of damaged cultural relics images. This paper briefly introduced the Criminisi image restoration algorithm and the structure tensor used to improve the algorithm in the digital cultural relics image restoration. A damaged cultural relics image and a complete image which was damaged by human were repaired respectively using the classical Criminisi image restoration algorithm and the improved structure tensor based repair algorithm on MATLAB software. The results showed that the Criminisi image restoration algorithm could be used to repair the damaged images of ancient fabrics. It was found that the classical image restoration algorithm had some shortcomings, such as inappropriate texture structure, obvious repair marks and addition of redundant information, but the improved algorithm effectively avoided the above shortcomings. The peak signal to noise ratio (SNR) of the complete image which was damaged by human was compared objectively, and it was found that the improved algorithm had better restoration performance.

Mathematical Methods in Image Processing: A Survey

Image processing is a fundamental field that plays a crucial role in various applications such as medical imaging, remote sensing, and computer vision. Mathematical methods form the foundation of image processing algorithms, providing the framework for understanding image properties and designing efficient processing techniques. This survey provides a comprehensive overview of the mathematical methods used in image processing, including digital image representation, image enhancement, restoration, and segmentation. Advanced mathematical methods such as sparse representations, variational methods, Markov random fields, and deep learning techniques are also discussed, highlighting their significance in advancing the field of image processing. The applications of mathematical methods in various domains, including medical imaging, satellite image analysis, forensic image analysis, and industrial image processing, are explored. Finally, the survey discusses the challenges faced by researchers in image processing, such as computational complexity, big data management, interdisciplinary research trends, and emerging technologies, and suggests future directions for research in this area.

Digital superresolution in seismic AVO inversion

The sparseness promoted by the total variation norm is utilized to achieve superresolution amplitude-variation-with-offset (AVO) inversion. The total variation norm promotes solutions that have constant values within unspecified regions and thus are well suited for an earth model consisting of layers bounded by faults and erosion surfaces. Algorithmic developments from digital image and video restoration are utilized to solve the geophysical problem. A spatial point spread function is used to model the resulting effect of wave propagation, migration, and processing. The methodology is compared to current alternatives and discussed in the context of AVO inversion. Good results are obtained in a Barents Sea test case.

Bleed-through cancellation in non-rigidly misaligned recto–verso archival manuscripts based on local registration

Ancient manuscripts written on both pages of the sheet are frequently affected by ink bleeding from the reverse side. This phenomenon produces a significant degradation of both the foreground text and the general appearance of the manuscript. Effective digital image restoration techniques may require the use of the content of both document sides, thus needing their perfect alignment. Although often available, recto and verso are usually not aligned, either for rigid misalignments occurring during acquisition, or for non-rigid deformations of the sheet. In this paper, we propose a novel method to restore color recto–verso manuscript images in a piecewise manner, without the need of a preliminary, global registration of the two sides. We assume that at the local level any deformation can be approximated by a displacement and subdivide the two images into small patches of same size. For each pair of patches at the same location, their relative shift is estimated by cross-correlation, thus allowing their straightforward alignment. A bleed-through removal algorithm is then applied to the registered patches. By spanning the entire images, this procedure returns free-of-interferences versions of the images in their original acquisition layout. The experiments show that the restoration results so obtained are better than those obtained with the classical approach that first registers the whole recto–verso pair and then performs restoration. Further advantages are a much lower computational cost, the possibility to manage non-global and non-rigid deformations, and the unaltered geometry and color appearance of the two restored images.

Imaging Analysis and Digital Restoration of the Holy Face of Manoppello—Part I

The Veil of Manoppello is an icon of the face of Christ (Holy Face). Its particular characteristic is being semitransparent. The face is visible on both sides (front–back) and, depending on the lighting and observation conditions, shows some differences in the anatomical details. An analysis of this icon has allowed us to clarify some aspects of the possible physical mechanism underlying its unusual optical behavior. It is a linen fiber fabric consisting of very thin threads with a thickness of about 0.1 mm, separated by distances even double the thickness of the threads, so that about 42% of the Veil is empty space. The fibers constituting the linen threads may have been cemented by an organic substance of chemical composition similar to cellulose, presumably starch, eliminating the air between them. Such a structure causes the optical behavior of the medium to be intermediate between those of a translucent medium (thin cemented linen threads) and a transparent one (empty space between the threads). The problem of digital image restoration in spatial terms has also been tackled, since the Holy Face is deformed due to distortions of the meshes of the Veil, caused by the yielding of the very fine structure of the fabric.

Unveiling the invisible: mathematical methods for restoring and interpreting illuminated manuscripts

The last 50 years have seen an impressive development of mathematical methods for the analysis and processing of digital images, mostly in the context of photography, biomedical imaging and various forms of engineering. The arts have been mostly overlooked in this process, apart from a few exceptional works in the last 10 years. With the rapid emergence of digitisation in the arts, however, the arts domain is becoming increasingly receptive to digital image processing methods and the importance of paying attention to this therefore increases. In this paper we discuss a range of mathematical methods for digital image restoration and digital visualisation for illuminated manuscripts. The latter provide an interesting opportunity for digital manipulation because they traditionally remain physically untouched. At the same time they also serve as an example for the possibilities mathematics and digital restoration offer as a generic and objective toolkit for the arts.

Non-Local Sparse Image Inpainting for Document Bleed-Through Removal

Bleed-through is a frequent, pervasive degradation in ancient manuscripts, which is caused by ink seeped from the opposite side of the sheet. Bleed-through, appearing as an extra interfering text, hinders document readability and makes it difficult to decipher the information contents. Digital image restoration techniques have been successfully employed to remove or significantly reduce this distortion. This paper proposes a two-step restoration method for documents affected by bleed-through, exploiting information from the recto and verso images. First, the bleed-through pixels are identified, based on a non-stationary, linear model of the two texts overlapped in the recto-verso pair. In the second step, a dictionary learning-based sparse image inpainting technique, with non-local patch grouping, is used to reconstruct the bleed-through-contaminated image information. An overcomplete sparse dictionary is learned from the bleed-through-free image patches, which is then used to estimate a befitting fill-in for the identified bleed-through pixels. The non-local patch similarity is employed in the sparse reconstruction of each patch, to enforce the local similarity. Thanks to the intrinsic image sparsity and non-local patch similarity, the natural texture of the background is well reproduced in the bleed-through areas, and even a possible overestimation of the bleed through pixels is effectively corrected, so that the original appearance of the document is preserved. We evaluate the performance of the proposed method on the images of a popular database of ancient documents, and the results validate the performance of the proposed method compared to the state of the art.

Computing Sparse GCD of Multivariate Polynomials via Polynomial Interpolation

The problem of computing the greatest common divisor (GCD) of multivariate polynomials, as one of the most important tasks of computer algebra and symbolic computation in more general scope, has been studied extensively since the beginning of the interdisciplinary of mathematics with computer science. For many real applications such as digital image restoration and enhancement, robust control theory of nonlinear systems, L1-norm convex optimization in compressed sensing techniques, as well as algebraic decoding of Reed-Solomon and BCH codes, the concept of sparse GCD plays a core role where only the greatest common divisors with much fewer terms than the original polynomials are of interest due to the nature of problems or data structures. This paper presents two methods via multivariate polynomial interpolation which are based on the variation of Zippel’s method and Ben-Or/Tiwari algorithm, respectively. To reduce computational complexity, probabilistic techniques and randomization are employed to deal with univariate GCD computation and univariate polynomial interpolation. The authors demonstrate the practical performance of our algorithms on a significant body of examples. The implemented experiment illustrates that our algorithms are efficient for a quite wide range of input.

A Review on Digital Image Restoration Process

A Review on Digital Image Restoration Process

Chebyshev-based technique for automated restoration of digital copies of faded photographic prints

We present a technique for automated restoration of digital images obtained from faded photographic prints. The proposed defading technique uses our early proposed image contrast enhancement algorithm based on a contrast measure of images in the Chebyshev moment transform domain. Obtained experimental results demonstrate some advantages of the technique as compared to other widely used image enhancement methods.

Improved Digital Image Restoration Algorithm Based on Criminisi

Improved Digital Image Restoration Algorithm Based on Criminisi

Method of Digital Hologram Coding–Decoding and Holographic Image Processing Based on the Gabor Wavelet

Special features of an algorithm for coding-decoding of digital particle holograms and restoration of holographic particle images based on the Gabor wavelet are considered. The method involves the application of the decoded wavelet coefficients for the subsequent restoration of images from digital holograms. Results of approbation of the method to numerically calculated holograms and holograms of plankton particles are presented.

Laplacian-Based Frequency Domain Filter for the Restoration of Digital Images Corrupted by Periodic Noise

This paper presents a new Laplacian-based frequency domain filter for the effective restoration of images corrupted by periodic noise. In the noise detection phase of the proposed algorithm, the Laplacian high-pass filter eases the thresholding-based noise detection process by highlighting noisy peak areas from uncorrupted areas of the origin-shifted Fourier-transformed corrupted image. The filtering phase of the proposed algorithm restores the detected noisy frequencies with nearest uncorrupted frequencies to ensure high fidelity in the restored image. Experimental analysis on different images reveals that the proposed algorithm produces better outputs in terms of subjective and objective metrics than other prominent algorithms.

An Integrated Repainting System for Digital Restoration of Vijayanagara Murals

An integrated repainting system is proposed in this paper for digital restoration of images of heritage murals, which have historical significance in their painting styles and ritualistic contents. The repainting system uses an ensemble of conventional image processing tools, in tandem with some state-of-the-art image rendition techniques, such as scaled bilateral filtering, source-constrained inpainting, tonal processing, and texture mapping based on gradient fusion. Murals that are old by nearly four centuries, have been imaged in situ from the walls of temples under a controlled environment, and then they have been fed to our repainting system. As the work of mural art is highly subjective, and so is its interpretation, a battery of tests for subjective evaluation has been performed to compare the different stages of restoration. Three different tournament strategies have been followed to make the test result devoid of any subjective bias as far as possible. The overall evaluation result is quite encouraging, as the restored images exhibit a gradually improving quality through the different stages of restoration.

Adaptive Switching Non-local Filter for the Restoration of Salt and Pepper Impulse-Corrupted Digital Images

The paper presents an effective nonlinear adaptive switching non-local filter for the restoration of impulse-corrupted digital images by using distinct impulse detection and correction stages. The correction scheme of the filter adaptively switches between details-preserving non-local mode and signal restoration-based local mode to facilitate high fidelity in the restored image. The non-local filtering operation replaces impulses with a remote pixel that better suits the local image conditions. The algorithm works in this non-local mode only when there are sufficient uncorrupted pixels in the local neighborhood of the corrupted pixel to be replaced. Otherwise, the algorithm replaces impulsive pixels with the median of the uncorrupted pixels from the local neighborhood. Experimental results from various impulse noise levels support the improved performance of the proposed algorithm over other algorithms both subjectively and objectively.

Image inpainting algorithm based on TV model and evolutionary algorithm

With the development of modern image processing techniques, the numbers of images increase at a high speed in network. As a new form of visual communication, image is widely used in network transmission. However, the image information would be lost after transmission. In view of this, we are motivated to restore the image to make it complete in an effective and efficient way in order to save the network bandwidth. At present, there are two main methods for digital image restoration, texture-based method and non-textured-based method. In the texture-based method, Criminisi algorithm is a widely used algorithm. However, the inaccurate completion order and the inefficiency in searching matching patches are two main limitations of Criminisi algorithm. To overcome these shortcomings, in this paper, an exemplar image completion based on evolutionary algorithm is proposed. In the non-textured-based method, total variation method is a typical algorithm. An improved total variation algorithm is proposed in this paper. In the improved algorithm, the diffusion coefficients are defined according to the distance and direction between the damaged pixel and its neighborhood pixel. Experimental results show that the proposed algorithms have better general performance in image completion. And these two new algorithms could improve the experience of network surfing and reduce the network communication cost.

An image inpainting method using pLSA-based search space estimation

In this paper, we present a novel exemplar-based image inpainting technique based on the local context measure of the target patch. Three main steps of the proposed method are determination of patch priority, the search space estimation for the candidate patches and the patch completion to fill in the unknown pixels of the target patch. In patch priority, we emphasize on the structure by the spatial relationship of neighborhood similar patches and kernel regression based local image structure. We find the search space, sub-regions of the entire source region similar to the region surrounding the target patch, to find the candidate patches. The said search space is estimated using probabilistic latent semantic analysis (pLSA). Last, we infer the unknown pixels of the target patch using pLSA-based context and histogram similarity measure between the target patch and the candidate patches. Experimental results are found to be good compared to the competitive methods and may be used for digital restoration of images of defective or damaged artifacts.

Quantification of nanoparticles at the single-cell level: an overview about state-of-the-art techniques and their limitations.

With the increasing production and use of engineered nanoparticles it is crucial that their interaction with biological systems is understood. Due to the small size of nanoparticles, their identification and localization within single cells is extremely challenging. Therefore, various cutting-edge techniques are required to detect and to quantify metals, metal oxides, magnetic, fluorescent, as well as electron-dense nanoparticles. Several techniques will be discussed in detail, such as inductively coupled plasma atomic emission spectroscopy, flow cytometry, laser scanning microscopy combined with digital image restoration, as well as quantitative analysis by means of stereology on transmission electron microscopy images. An overview will be given regarding the advantages of those visualization/quantification systems, including a thorough discussion about limitations and pitfalls.