Digital Image Restoration Research Articles

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.

An intelligent lithology recognition system for continental shale by using digital coring images and convolutional neural networks

Lithology identification is a critical component of reservoir evaluation and hydrocarbon development. However, the continental shale rocks, characterized by complex lithologies and developed laminations, present new challenges to traditional lithology identification techniques. In this study, we develop a centimeter-level intelligent identification system for continental shale lithology, leveraging digital image preprocessing and deep learning methods. Specifically, we construct a dataset from 400 m of drilling cores, encompassing 24 types of lithology and 152 lithological transitions. The downsampling and oversampling methods are employed to balance the dataset. The digital image denoising and restoration techniques are then utilized to enhance rock image features. Different neural network models are examined to obtain the optimum predictive results. The results indicate that data balancing improves the TOP1 and TOP5 accuracies by 10% and 5%, respectively. Gaussian low-pass filter denoising, in comparison to other methods, increases the TOP1 and TOP5 accuracies by 2%, establishing it as the superior denoising technique for lithology recognition. Considering both computational efficiency and prediction accuracy, EfficientNet emerges as the most effective model for continental shale image recognition. The optimized model attains up to 60% TOP1 and 95% TOP5 across 22 categories in drilling core samples. This study delves into the intelligent identification of macroscopic drilling core images of continental shale, and the findings can offer technical support for the interpretation and evaluation of continental shale reservoirs.

Dunhuang murals image restoration method based on generative adversarial network

AbstractMurals are an important part of China’s cultural heritage. After more than a 1000 years of exposure to the sun and wind, most of these ancient murals have become mottled, with damage such as cracking, mold, and even large-scale detachment. It is an urgent work to restore these damaged murals. The technique of digital restoration of mural images refers to the reconstruction of structures and textures to virtually fill in the damaged areas of the image. Existing digital restoration methods have the problems of incomplete restoration and distortion of local details. In this paper, we propose a generative adversarial network model combining a parallel dual convolutional feature extraction depth generator and a ternary heterogeneous joint discriminator. The generator network is designed with the mechanism of parallel extraction of image features by vanilla convolution and dilated convolution, capturing multi-scale features simultaneously, and reasonable parameter settings reduce the loss of image information. A pixel-level discriminator is proposed to identify the pixel-level defects of the captured image, and its joint global discriminator and local discriminator discriminate the generated image at different levels and granularities. In this paper, we create the Dunhuang murals dataset and validate our method on this dataset, and the experimental results show that the method of this paper has an overall improvement in the evaluation metrics of PSNR and SSIM compared with the comparative methods. The restored resultant image is more in line with the subjective vision of human beings, which achieves the effective restoration of mural images.

Current Research and Future Directions in Image Restoration Technology: An Emerging Field

Abstract: A review and analysis of digital image restoration are provided in this work. The goal of image restoration is to enhance the quality of an image by understanding the physical process that created it. The purpose of picture restoration is to cover up or correct flaws that lower an image's quality. Motion blur, noise, and difficulty focusing the camera are just a few examples of how degradation can manifest itself. When there is motion blur, for example, it is possible to "undo" the blurring function and return the image to its previous state. The best course of action when noise distorts an image is to fix the damage it causes. In contrast to image enhancement, which focuses more on highlighting or extracting picture features than on restoring degradations, image restoration restores degraded images. While the mathematical representation of enhancement criteria is challenging, image restoration difficulties may be properly quantified. Restoration of images began in the 1950s. Application areas for image restoration include consumer photography, legal investigations, filmmaking and rivalries, image and video decoding, and scientific research. Image reconstruction in radio astronomy, radar imaging, and tomography is the principal area of use. This study proposal explores various image restoration methods and discusses the value of image restoration techniques.

Retracted: Digital Image Restoration Based on Multicontour Batch Scanning

Retracted: Digital Image Restoration Based on Multicontour Batch Scanning

Adaptive Smoothing for Visual Improvement of Image Quality via the p(x)-Laplacian Operator Effects of the p(x)-Laplacian Smoothing Operator on Digital Image Restoration: Contribution to an Adaptive Control Criterion

This article concerns the improvement of digital image quality using mathematical tools such as nonlinear partial differential operators. In this paper, to perform smoothing on digital images, we propose to use the p(x)-Laplacian operator. Its smoothing power plays a main role in the restoration process. This enables us to dynamically process certain areas of an image. We used a mathematical model of image regularisation that is described by a nonlinear diffusion Equation (this diffusion is modelled by the p(x)-Laplacian operator). We implemented the continuous model in order to observe the steps of the regularisation process to understand the effects of the different parameters of the model on an image. This will enable parameters to be used and adapted in order to provide a proposed solution.

Infrared Thermography For Seal Defects Detection On Packaged Products: Unbalanced Machine Learning Classification With Iterative Digital Image Restoration

Non-destructive and online defect detection on seals is increasingly being deployed in packaging processes, especially for food and pharmaceutical products. It is a key control step in these processes as it curtails the costs of these defects.
 To address this cause, this paper highlights a combination of two cost-effective methods, namely machine learning algorithms and infrared thermography. Expectations can, however, be restricted when the training data is small, unbalanced, and subject to optical imperfections.
 This paper proposes a classification method that tackles these limitations. Its accuracy exceeds 93% with two small training sets, including 2.5 to 10 times fewer negatives. Its algorithm has a low computational cost compared to deep learning approaches, and does not need any prior statistical studies on defects characterization.

(Retracted) Mural digital image restoration technology and stitching evaluation model based on machine learning

Mural painting refers to art on the wall, which has high aesthetic value. However, due to its vulnerability to damage, digital image restoration and stitching techniques are now used to permanently preserve mural images. The purpose of our study is to select a more suitable specific calculation method by studying machine learning (ML) methods and conduct in-depth research on mural digital image restoration technology and stitching evaluation, so that it can better serve the restoration and splicing of the current mural digital images. Based on the experiments, it can be seen that the respondents in hall A had a high degree of recognition for digital image restoration and stitching technology. There were 176 respondents, and only 29 people believed that digital image restoration was not important; respondents in hall C did not agree with the digital image restoration technology as much as hall A, only 132 people thought it was important, and 64 people thought it was not important. It can be seen that the model establishment of mural digital image restoration technology and splicing evaluation is deeply affected by the knowledge level of the evaluator. The experimental results of our study showed that the research process of mural digital image restoration technology and stitching evaluation based on ML method was more effective than other methods of analyzing experimental data.

Semi‐tensor product of quaternion matrices and its application

Unlike real or complex numbers, quaternion multiplication does not satisfy the commutative law. This makes it impossible to generalize some conclusions on semi‐tensor product to quaternion directly, which is valid on real or complex number fields. In this paper, we define semi‐tensor product of quaternion matrices and study the properties of semi‐tensor product of quaternion matrices, and the conclusion is applied to the color digital image restoration model.

Bilinear and Bicubic Interpolations for Image Presentation of Mechanical Stress and Temperature Distribution

Bilinear and bicubic interpolations were often used in digital elevation models (DEMs), image scaling, and image restoration, with the aid of spatial transform techniques. This paper resorts to bilinear and bicubic interpolations, along with the spatial transform of images, to present the temperature distribution on a plate with a circular hole. The Dirichlet boundary conditions were applied, a rectangular grid was created, and the nodal values were calculated using the finite difference method (FDM). These methods were also employed to represent the mechanical stress distribution on a plate with a circular hole, under the presence of uniaxial stress. In this case, the nodal values were calculated using the analytical method. Experimental results show that bicubic interpolation generated continuous contours, while bilinear interpolation had a discontinuity in some cases. The results were comparative to images for similar cases when solved through ANSYS.

Cover Design of Public Service Advertisement Based on Deep Image Rendering Technology

With the development of digital multimedia technology, public service advertisement covers are mostly released in the form of animation, mainly in the form of two-dimensional and three-dimensional animations, disseminating artistic energy through artistic expression, and unknowingly imparting laws, rules and morals to the public, promoting national traditional culture, and establish a good social image through public service advertisements. With the rapid development of image processing technology and the continuous upgrading of display devices, people have begun to pursue more three-dimensional and immersive digital display technologies. According to the Criminisi algorithm, it mainly repairs the edge defects in the source image, and then updates the pixel confidence of the repaired image to start the next repair until the repair is completed. In order to make the public service advertisement cover design meet the needs of the market, the advertisement cover is designed through image drawing technology, the collected advertisement video is passed through Criminisi digital image restoration technology and its classification, and the improved Criminisi algorithm is explained to repair the advertisement image, and then the mean shift Clustering (Mean Shift Clustering) algorithm iteratively counts the processed images. The processing of the public service advertisement cover through the depth image rendering technology greatly reduces the repair rate without affecting the repair quality, restores the damaged image more accurately, and can reduce the video point rate. It can be increased to 0.87, saving 29% of the bit rate.

Digital Image Restoration Based on Multicontour Batch Scanning.

In order to explore the problem of digital image restoration, the authors propose a research on digital image restoration based on multicontour batch scanning. This method recommends key technical problems and solutions based on information represented by multicontour batch scans, exploring research in digital image restoration. Research has shown that the research on digital image restoration based on multicontour batch scanning is about 40% more efficient than traditional methods. Aiming at the new application of digital image inpainting, the application of image inpainting in image compression is studied in depth, and the technical principles of image inpainting and image compression are complemented.

Solving Quaternion Linear System Based on Semi-Tensor Product of Quaternion Matrices

In this paper, we use semi-tensor product of quaternion matrices, L-representation of quaternion matrices, and GH-representation of special quaternion matrices such as quaternion (anti)-centrosymmetric matrices to solve the special solutions of quaternion matrix equation. Based on semi-tensor product of quaternion matrices and the structure matrix of the multiplication of quaternion, we propose the vector representation operation conclusion of quaternion matrices, and study the different matrix representations of quaternion matrices. Then the problem of the quaternion matrix equation is transformed into the corresponding problem in the real number fields by using vector representation and L-representation of quaternion matrices, combined with the special structure of (anti)-centrosymmetric matrices, the independent elements are extracted by GH-representation method, so as to reduce the number of variables to be calculated and improve the calculation accuracy. Finally, the effectiveness of the method is verified by numerical examples, and the time comparison with the two existing algorithms is carried out. The algorithm in this paper is also applied in a centrosymmetric color digital image restoration model.

Intelligent Restoration Technology of Mural Digital Image Based on Machine Learning Algorithm

Influenced by the natural environment and preservation conditions, the frescoes in the monastery were damaged to some extent. In order to solve the fresco image restoration conversion and structural decomposition defects, an intelligent fresco digital image restoration technique based on machine learning algorithm is proposed. Firstly, the digital image information of mural is collected by a scanning program, and the data in the database is extracted. The mean filter template is used to restore the color of mural digital image, and the Gaussian template is used to restore the color of image details. The two are superimposed to ensure that the image is not affected by distortion and halo, so as to ensure the clarity of image and boundary. The depth learning model of local fuzzy feature restoration of mural digital image is obtained by using multimodal feature decomposition method, and the intelligent restoration of mural digital image is realized by this model. The experimental results show that this method can effectively repair the local fuzzy features of mural digital image. The repair precision is more than 95.7% and fills in the missing information from the image, and the image quality is good.

Research on Virtual Color Restoration of Complex Building System Based on Discrete Wavelet Transform

At present, image restoration has become a research hotspot in computer vision. The purpose of digital image restoration is to restore the lost information of the image or remove redundant objects without destroying the integrity and visual effects of the image. The operation of user interactive color migration is troublesome, resulting in low efficiency. And, when there are many kinds of colors, it is prone to errors. In response to these problems, this paper proposes automatic selection of sample color migration. Considering that the respective gray-scale histograms of the visual source image and the target image are approximately normal distributions, this paper takes the peak point as the mean value of the normal distribution to construct the objective function. We find all the required partitions according to the user’s needs and use the center points in these partitions as the initial clustering centers of the fuzzy C-means (FCM) algorithm to complete the automatic clustering of the two images. This paper selects representative pixels as sample blocks to realize automatic matching of sample blocks in the two images and complete the color migration of the entire image. We introduced the curvature into the energy functional of the p-harmonic model. According to whether there is noise in the image, a new wavelet domain image restoration model is proposed. According to the established model, the Euler–Lagrange equation is derived by the variational method, the corresponding diffusion equation is established, and the model is analyzed and numerically solved in detail to obtain the restored image. The results show that the combination of image sample texture synthesis and segmentation matching method used in this paper can effectively solve the problem of color unevenness. This not only saves the time for mural restoration but also improves the quality of murals, thereby achieving more realistic visual effects and connectivity.

DIGITAL IMAGE RESTORATION USING LINEAR PDE-BASED FILTERING MODELS

The linear partial differential equation (PDE) - based models for Gaussian noise removal are discussed in this paper. The digital image denoising and restoration solutions based on linear diffusion equations are surveyed first. Then, our own contributions in this image processing domain, representing some effective linear PDE-based filtering models based on hyperbolic and stochastic differential equations, are presented. The results of our denoising experiments are also provided in this article.

Defective Chinese Painting Digital Image Restoration Using Improved BSCB Algorithm

Defective Chinese Painting Digital Image Restoration Using Improved BSCB Algorithm

Research on Error Concealment for Video Communication

The video sequence restoration described in this article has been extended to many areas of our lives today. People use it to perform various restoration tasks, making it possible for some pictures that were impossible to restore in the past. This paper uses experimental methods to study the error concealment and traditional image restoration in video compression, and mainly studies the CDD model algorithm, TV model algorithm and error mode. This article also has a very important meaning for the related research of video communication error control, that is, the research of digital image restoration technology, in the post-processing of film and television works, the fuzzification of network video, and the restoration of old documentaries.

Digital watermarking algorithm for multi-sampling compressed sensing in measurement domain

Compressed sensing technology is widely used in the field of digital watermarking and traditional algorithms lack proper selection of carrier data, resulting in excessive computing costs. The algorithm in this paper mainly focuses on the measured values of different regions, uses the clustering algorithm to select the appropriate image blocks, adjusts the sampling rate to re-measure the background image blocks, obtains new measurement values, and then performs digital watermark embedding and carrier image restoration. The experiment shows this method improved transparency, while possessing certain robustness and resistance to extraction.

Restoration of non-structural damaged murals in Shenzhen Bao\u2019an based on a generator\u2013discriminator network

Shenzhen is a modern metropolis, but it hides a variety of valuable cultural heritage, such as ancient murals. How to effectively preserve and repair the murals is a worthy of discussion question. Here, we propose a generation-discriminator network model based on artificial intelligence algorithms to perform digital image restoration of ancient damaged murals. In adversarial learning, this study optimizes the discriminative network model. First, the real mural images and damaged images are spliced together as input to the discriminator network. The network uses a 5-layer encoder unit to down-sample the 1024 × 1024 × 3 image to 32 × 32 × 256. Then, we connect a layer of ZeroPadding2D to expand the image to 34 × 34 × 256, and pass the Conv2D layer, down-sample to 31 × 31 × 256, perform batch normalization, and repeat the above steps to get a 30 × 30 × 1 matrix. Finally, this part of the loss is emphasized in the loss function as needed to improve the texture detail information of the image generated by the Generator. The experimental results show that compared with the traditional algorithm, the PSNR value of the algorithm proposed in this paper can be increased by 5.86 db at most. The SSIM value increased by 0.13. Judging from subjective vision. The proposed algorithm can effectively repair damaged murals with dot-like damage and complex texture structures. The algorithm we proposed may be helpful for the digital restoration of ancient murals, and may also provide reference for mural restoration workers.