MIDNet:Deblurring Network for Material Microstructure Images

Scanning electron microscopy(SEM)is a crucial tool in the field of materials science,providing valuable insightsinto the microstructural characteristics of materials.Unfortunately,SEM images often suffer from blurrinesscaused by improper hardware calibration or imaging automation errors,which present challenges in analyzingand interpretingmaterial characteristics.Consequently,rectifying the blurring of these images assumes paramountsignificance to enable subsequent analysis.To address this issue,we introduce a Material Images DeblurringNetwork(MIDNet)built upon the foundation of the Nonlinear Activation Free Network(NAFNet).MIDNetis meticulously tailored to address the blurring in images capturing the microstructure of materials.The keycontributions include enhancing the NAFNet architecture for better feature extraction and representation,integratinga novel soft attention mechanism to uncover important correlations between encoder and decoder,andintroducing newmulti-loss functions to improve training effectiveness and overallmodel performance.We conducta comprehensive set of experiments utilizing the material blurry dataset and compare them to several state-of-theartdeblurring methods.The experimental results demonstrate the applicability and effectiveness of MIDNet in thedomain of deblurring material microstructure images,with a PSNR(Peak Signal-to-Noise Ratio)reaching 35.26 dBand an SSIM(Structural Similarity)of 0.946.Our dataset is available at:https://github.com/woshigui/MIDNet.

Remote Sensing Image Deblurring Based on Grid Computation

In general, there is a demand for real-time processing of mass quantity remote sensing images. However, the task is not only data-intensive but also computating-intensive. Distributed processing is a hot topic in remote sensing processing and image deblurring is also one of the most important needs. In order to satisfy the demand for quick proc- essing and deblurring of mass quantity satellite images, we developed a distributed, grid computation-based platform as well as a corresponding middleware for grid computation. Both a constrained power spectrum equalization algorithm and effective block processing measures, which can avoid boundary effect, were applied during the processing. The re- sult is satisfactory since computation efficiency and visual effect were greatly improved. It can be concluded that the technology of spatial information grids is effective for mass quantity remote sensing image processing.

Deblurring Texture Extraction from Digital Aerial Image by Reforming “Steep Edge” Curve

Texture extract from digital aerial image is widely used in three-dimensional city modeling to generate “photo-realistic” views. In this paper, a method based on reforming “Steep edge” curve, which clearly explains how the diffraction of the sunlight makes digital aerial image blurring, is proposed to deblur the texture extraction from digital aerial image, and the experiment shows a good result in visualization and automation.

Blind Deblurring Based on L_0 Norm from Salient Edges

Motion deblurring is a basic problem in the field of image processing and analysis. This paper proposes a new method of single image blind deblurring which can be significant to kernel estimation and non-blind deconvolution. Experiments show that the details of the image destroy the structure of the kernel, especially when the blur kernel is large. So we extract the image structure with salient edges by the method based on RTV. In addition, the traditional method for motion blur kernel estimation based on sparse priors is conducive to gain a sparse blur kernel. But these priors do not ensure the continuity of blur kernel and sometimes induce noisy estimated results. Therefore we propose the kernel refinement method based on L0 to overcome the above shortcomings. In terms of non-blind deconvolution we adopt the L1/L2 regularization term. Compared with the traditional method, the method based on L1/L2 norm has better adaptability to image structure, and the constructed energy functional can better describe the sharp image. For this model, an effective algorithm is presented based on alternating minimization algorithm.

A survey on facial image deblurring

When a facial image is blurred,it significantly affects high-level vision tasks such as face recognition.The purpose of facial image deblurring is to recover a clear image from a blurry input image,which can improve the recognition accuracy,etc.However,general deblurring methods do not perform well on facial images.Therefore,some face deblurring methods have been proposed to improve performance by adding semantic or structural information as specific priors according to the characteristics of the facial images.In this paper,we survey and summarize recently published methods for facial image deblurring,most of which are based on deep learning.First,we provide a brief introduction to the modeling of image blurring.Next,we summarize face deblurring methods into two categories:model-based methods and deep learning-based methods.Furthermore,we summarize the datasets,loss functions,and performance evaluation metrics commonly used in the neural network training process.We show the performance of classical methods on these datasets and metrics and provide a brief discussion on the differences between model-based and learning-based methods.Finally,we discuss the current challenges and possible future research directions.

Non-Blind Image Deblurring via Shear Total Variation Norm

In this paper, we propose a novel shear gradient operator by combining the shear and gradient operators. The shear gradient operator performs well to capture diverse directional information in the image gradient domain. Based on the shear gradient operator, we extend the total variation(TV) norm to the shear total variation(STV) norm by adding two shear gradient terms. Subsequently, we introduce a shear total variation deblurring model. Experimental results are provided to validate the ability of the STV norm to capture the detailed information. Leveraging the Block Circulant with Circulant Blocks(BCCB) structure of the shear gradient matrices, the alternating direction method of multipliers(ADMM) algorithm can be used to solve the proposed model efficiently. Numerous experiments are presented to verify the performance of our algorithm for non-blind image deblurring.

Resolution enhancement with deblurring by pixel reassignment

Improving the spatial resolution of a fluorescence microscope has been an ongoing challenge in the imaging community.To address this challenge,a variety of approaches have been taken,ranging from instrumentation development to image postprocessing.An example of the latter is deconvolution,where images are numerically deblurred based on a knowledge of the microscope point spread function.However,deconvolution can easily lead to noise-amplification artifacts.Deblurring by postprocessing can also lead to negativities or fail to conserve local linearity between sample and image.We describe here a simple image deblurring algorithm based on pixel reassignment that inherently avoids such artifacts and can be applied to general microscope modalities and fluorophore types.Our algorithm helps distinguish nearby fluorophores,even when these are separated by distances smaller than the conventional resolution limit,helping facilitate,for example,the application of single-molecule localization microscopy in dense samples.We demonstrate the versatility and performance of our algorithm under a variety of imaging conditions.

Algorithm for the Removing Uniformed Motion Blur

Motion deblurring is one of the basic problems inthe field of image processing. This paper summarizes the mathematical basis of the previous work and presents a deblurringmethod that can improve the estimation of the motion blurkernel and obtain a better result than the traditional methods.Experiments show the motion blur kernel loses some important and useful properties during the estimation of the kernel which may cause a bad estimation and increase the ringingartifacts. Considering that the kernel is provided by the motion of the imaging sensor during the exposure and that the kernel shows the trace of the motion, this paper ensures the physical meaning of the kernel such as the continuity and the center of thekernel during the iterative process. By adding a post process to the estimation of the motion blur kernel, we remove some discrete points and make use of the centralizationof the kernel in order to accurate the estimation. The experiment shows the existence of the post process improves the effect of the estimation of the kernel and provides a better result with the clear edges.

Low Rank and Total Variation Based Two-Phase Method for Image Deblurring with Salt-and-Pepper Impulse Noise

Although there are many effective methods for removing impulse noise in image restoration,there is still much room for improvement.In this paper,we propose a new two-phase method for solving such a problem,which combines the nuclear norm and the total variation regularization with box constraint.The popular alternating direction method of multipliers and the proximal alternating direction method of multipliers are employed to solve this problem.Compared with other algorithms,the obtained algorithm has an explicit solution at each step.Numerical experiments demonstrate that the proposed method performs better than the stateof-the-art methods in terms of both subjective and objective evaluations.

Saturation-Value Blind Color Image Deblurring with Geometric Spatial-Feature Prior

Blind deblurring for color images has long been a challenging computer vision task.The intrinsic color structures within image channels have typically been disregarded in many excellent works.We investigate employing regularizations in the hue,saturation,and value(HSV)color space via the quaternion framework in order to better retain the internal relationship among the multiple channels and reduce color distortions and color artifacts.We observe that a geometric spatial-feature prior utilized in the intermediate latent image successfully enhances the kernel accuracy for the blind deblurring variational models,preserving the salient edges while decreasing the unfavorable structures.Motivated by this,we develop a saturation-value geometric spatial-feature prior in the HSV color space via the quaternion framework for blind color image deblurring,which facilitates blur kernel estimation.An alternating optimization strategy combined with a primal-dual projected gradient method can effectively solve this novel proposed model.Extensive experimental results show that our model outperforms state-of-the-art methods in blind color image deblurring by a wide margin,demonstrating the effectiveness of the proposed model.

Non-Blind Image Deblurring Method Using Shear High Order Total Variation Norm

In this paper,we propose a shear high-order gradient(SHOG)operator by combining the shear operator and high-order gradient(HOG)operator.Compared with the HOG operator,the proposed SHOG operator can incorporate more directionality and detect more abundant edge information.Based on the SHOG operator,we extend the total variation(TV)norm to shear high-order total variation(SHOTV),and then propose a SHOTV deblurring model.We also study some properties of the SHOG operator,and show that the SHOG matrices are Block Circulant with Circulant Blocks(BCCB)when the shear angle isπ/4.The proposed model is solved efficiently by the alternating direction method of multipliers(ADMM).Experimental results demonstrate that the proposed method outperforms some state-of-the-art non-blind deblurring methods in both objective and perceptual quality.

Progressive edge-sensing dynamic scene deblurring

Deblurring images of dynamic scenes is a challenging task because blurring occurs due to a combination of many factors.In recent years,the use of multi-scale pyramid methods to recover high-resolution sharp images has been extensively studied.We have made improvements to the lack of detail recovery in the cascade structure through a network using progressive integration of data streams.Our new multi-scale structure and edge feature perception design deals with changes in blurring at different spatial scales and enhances the sensitivity of the network to blurred edges.The coarse-to-fine architecture restores the image structure,first performing global adjustments,and then performing local refinement.In this way,not only is global correlation considered,but also residual information is used to significantly improve image restoration and enhance texture details.Experimental results show quantitative and qualitative improvements over existing methods.

Incorporating the Maximum Entropy on the Mean Framework with Kernel Error for Robust Non-Blind Image Deblurring

Non-blind deblurring is crucial in image restoration.While most previous works assume that the exact blurring kernel is known,this is often not the case in prac-tice.The blurring kernel is most likely estimated by a blind deblurring method and is not error-free.In this work,we incorporate a kernel error term into an advanced non-blind deblurring method to recover the clear image with an inaccurately estimated kernel.Based on the celebrated principle of Maximum Entropy on the Mean(MEM),the regularization at the level of the probability distribution of images is carefully com-bined with the classical total variation regularizer at the level of image/kernel.Exten-sive experiments show clearly the effectiveness of the proposed method in the pres-ence of kernel error.As a traditional method,the proposed method is even better than some of the state-of-the-art deep-learning-based methods.We also demonstrate the potential of combining the MEM framework with classical regularization approaches in image deblurring,which is extremely inspiring for other related works.

An Energy Regularization Method for the Backward Diffusion Problem and its Applications to Image Deblurring

For the backward diffusion equation,a stable discrete energy regularization algorithm is proposed.Existence and uniqueness of the numerical solution are given.Moreover,the error between the solution of the given backward diffusion equation and the numerical solution via the regularization method can be estimated.Some numerical experiments illustrate the efficiency of the method,and its application in image deblurring.

Total Variation Based Parameter-Free Model for Impulse Noise Removal

We propose a new two-phase method for reconstruction of blurred im-ages corrupted by impulse noise.In the first phase,we use a noise detector to iden-tify the pixels that are contaminated by noise,and then,in the second phase,we reconstruct the noisy pixels by solving an equality constrained total variation mini-mization problem that preserves the exact values of the noise-free pixels.For images that are only corrupted by impulse noise(i.e.,not blurred)we apply the semismooth Newton’s method to a reduced problem,and if the images are also blurred,we solve the equality constrained reconstruction problem using a first-order primal-dual algo-rithm.The proposed model improves the computational efficiency(in the denoising case)and has the advantage of being regularization parameter-free.Our numerical results suggest that the method is competitive in terms of its restoration capabilities with respect to the other two-phase methods.

Several Variants of the Primal-Dual Hybrid Gradient Algorithm with Applications

By reviewing the primal-dual hybrid gradient algorithm(PDHG)pro-posed by He,You and Yuan(SIAM J.Image Sci.,7(4)(2014),pp.2526–2537),in this paper we introduce four improved schemes for solving a class of saddle-point problems.Convergence properties of the proposed algorithms are ensured based on weak assumptions,where none of the objective functions are assumed to be strongly convex but the step-sizes in the primal-dual updates are more flexible than the pre-vious.By making use of variational analysis,the global convergence and sublinear convergence rate in the ergodic/nonergodic sense are established,and the numer-ical efficiency of our algorithms is verified by testing an image deblurring problem compared with several existing algorithms.

New Splitting Algorithms for Multiplicative Noise Removal Based on Aubert-Aujol Model

In this paper,we propose new algorithms for multiplicative noise removal based on the Aubert-Aujol(AA)model.By introducing a constraint from the forward model with an auxiliary variable for the noise,the NEMA(short for Noise Estimate based Multiplicative noise removal by alternating direction method of multipliers(ADMM))is firstly given.To further reduce the computational cost,an additional proximal term is considered for the subproblem with regard to the original variable,the NEMA_(f)(short for a variant of NEMA with fully splitting form)is further proposed.We conduct numerous experiments to show the convergence and performance of the proposed algorithms.Namely,the restoration results by the proposed algorithms are better in terms of SNRs for image deblurring than other compared methods including two popular algorithms for AA model and three algorithms of its convex variants.