Non-blind Deblurring Research Articles

Blind deblurring text images via Beltrami regularization

This article proposes a blind image deblurring model based on Beltrami regularization. The existence and uniqueness of the Beltrami model are proved, and we perform a theoretical analysis of the convergence and error estimation of the algorithm for solving the Beltrami regularization model. Meanwhile, we apply a half-quadratic splitting algorithm to solve it and obtain the blur kernel of the blurred image. Furthermore, we get a clean image through some non-blind deblurring algorithms. Finally, we compare this with the state-of-art methods, and our algorithm has specific performance improvements and advantages in the text images.© 2024 Elsevier Ltd. All rights reserved.

Local Extremum Constrained Total Variation Model for Natural and Hyperspectral Image Non-Blind Deblurring

Local Extremum Constrained Total Variation Model for Natural and Hyperspectral Image Non-Blind Deblurring

Out-of-Focus Image Deblurring for Mobile Display Vision Inspection

In vision inspection tasks, moiré patterns caused by frequency aliasing can severely degrade image quality. To prevent moiré patterns, we used images that were intentionally out-of-focused, and we performed deblurring to restore details during the acquisition of the images. As existing deblurring methods fail to output satisfactory results for low-contrast Mura images, we applied some simple techniques, minimum-maximum normalization, and edge mask fine-tuning to one of the state-of-the-art non-blind deblurring methods by utilizing parametric generalized Gaussian kernels. Structural image details were preserved through edge mask fine-tuning, and image contrast was improved with minimum-maximum normalization. By parameterizing the blur kernel as a generalized Gaussian kernel, we greatly improved the robustness of the blind image deblurring. We evaluated the effects of each module by conducting thorough experiments. The proposed method showed better performance than existing blind deblurring methods for blur-specific no-reference metrics, the image profile, and frequency domain analysis.

INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-Blind Image Deblurring in Low-Light Conditions

Under low-light environment, handheld photography suffers from severe camera shake under long exposure settings. Although existing deblurring algorithms have shown promising performance on well-exposed blurry images, they still cannot cope with low-light snapshots. Sophisticated noise and saturation regions are two dominating challenges in practical low-light deblurring: the former violates the Gaussian or Poisson assumption widely used in most existing algorithms and thus degrades their performance badly, while the latter introduces non-linearity to the classical convolution-based blurring model and makes the deblurring task even challenging. In this work, we propose a novel non-blind deblurring method dubbed image and feature space Wiener deconvolution network (INFWIDE) to tackle these problems systematically. In terms of algorithm design, INFWIDE proposes a two-branch architecture, which explicitly removes noise and hallucinates saturated regions in the image space and suppresses ringing artifacts in the feature space, and integrates the two complementary outputs with a subtle multi-scale fusion network for high quality night photograph deblurring. For effective network training, we design a set of loss functions integrating a forward imaging model and backward reconstruction to form a close-loop regularization to secure good convergence of the deep neural network. Further, to optimize INFWIDE's applicability in real low-light conditions, a physical-process-based low-light noise model is employed to synthesize realistic noisy night photographs for model training. Taking advantage of the traditional Wiener deconvolution algorithm's physically driven characteristics and deep neural network's representation ability, INFWIDE can recover fine details while suppressing the unpleasant artifacts during deblurring. Extensive experiments on synthetic data and real data demonstrate the superior performance of the proposed approach.

English

Image blurring refers to the degradation of an image wherein the image's overall sharpness decreases. Image blurring is caused by several factors. Additionally, during the image acquisition process, noise may get added to the image. Such a noisy and blurred image can be represented as the image resulting from the convolution of the original image with the associated point spread function, along with additive noise. However, the blurred image often contains inadequate information to uniquely determine the plausible original image. Based on the availability of blurring information, image deblurring methods can be classified as blind and non-blind. In non-blind image deblurring, some prior information is known regarding the corresponding point spread function and the added noise. The objective of this study is to determine the effectiveness of non-blind image deblurring methods with respect to the identification and elimination of noise present in blurred images. In this study, three non-blind image deblurring methods, namely Wiener deconvolution, Lucy-Richardson deconvolution, and regularized deconvolution were comparatively analyzed for noisy images featuring salt-and-pepper noise. Two types of blurring effects were simulated, namely motion blurring and Gaussian blurring. The said three non-blind deblurring methods were applied under two scenarios: direct deblurring of noisy blurred images and deblurring of images after denoising through the application of the adaptive median filter. The obtained results were then compared for each scenario to determine the best approach for deblurring noisy images.

Robust Blind Deblurring Under Stripe Noise for Remote Sensing Images

The blind image deblurring methods have achieved great progress for Gaussian random noise. Few works have paid attention to the image deblurring under the structural noise, which is a very common degradation in multi-detectors imaging systems. This paper considers the practical yet challenging problem of blind deblurring in presence of the line-pattern stripe noise for remote sensing images. To overcome this issue, we explicitly formulate the structural noise into a novel and robust blind image deblurring framework. We observe that the structural line-pattern stripe noise would deteriorate both the kernel estimation and non-blind deblurring, and propose a three-stage restoration framework to progressively estimate the blur kernel and clean image. Specifically, we first estimate an intermediate blur kernel by getting rid of the negative influence of the stripe noise in the unidirectional gradient domain. Next, a learning-based kernel refinement network is introduced to rectify the missing details of the inaccurate kernel. Finally, a low-rank decomposition-based non-blind deblurring model is proposed to simultaneously estimate the clean image and stripe noise. Experimental results on real and synthetic datasets demonstrate that the proposed RBDS method outperforms the state-of-the-art blind deblurring methods.

Fast Motion-Deblurring of IR Images

Camera gimbal systems pervade various applications such as navigation, target tracking, security and surveillance. The need for higher steering rate (rotation angle per second) of gimbal often results in motion blur in the captured video frames. Motion deblurring in real-time is difficult with existing blind restoration methods which incur large execution times while attempting to retrieve latent images from blurry inputs using high-dimensional optimization. On the other hand, deep learning methods for motion deblurring, though fast, do not generalize satisfactorily with domain shifts. In this work, we address the problem of real-time motion deblurring in infrared (IR) images captured by a real gimbal-based system. We propose two blur-kernel estimation methods and reveal how <i>a priori</i> knowledge of the blur-kernel can be used in conjunction with non-blind deblurring methods to achieve real-time performance. We experimentally show that, in comparison to the state-of-the-art techniques in deblurring, our method is better-suited for practical gimbal-based imaging systems.

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 is [see formula in PDF]. 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.

Investigation of patch-based modulation transfer function (MTF) prediction framework in radiography

The modulation transfer function (MTF) is a well-established in imaging systems and has played a significant role in assessing the performance of information transfer in accordance with the spatial frequency. Exist methods for measuring MTF require a pinhole, or a slit camera, an edge phantom, etc. These depend on the imaging system and exposure conditions. To overcome these problems, we investigated the proposed patch-based MTF prediction framework using a deep-learning algorithm in radiography. This proposed method directly allows for the predicted MTF from a single image without the need for additional devices and work. We used 800 to 1500 projections for supervised learning and selected many patches, including the edge information from input image for predicting the MTF, for which we used the Kendall's rank correlation. The quantitative evaluation performed included an intensity profile and a root-mean square error. In addition, we implemented the nonblind deblurring process to verify its image performance using the predicted MTF. Our methods successfully predicted the MTF and restored them from the degraded image without such artifacts as the ringing artifact that can occur when using the incorrect blur kernel. Consequently, the simulation and experimental results indicate that the proposed framework is useful to predicting the accurate MTF and is effective in improving radiographic image quality.

Nonuniform blind deblurring for single images based on adaptive edge-enhanced regularization

Natural images inevitably suffer from spatially variant blur caused by the relative motion between a camera and objects. We present an effective and efficient patch-wise edge-enhanced image regularization and a robust kernel similarity constraint to perform an accurate kernel estimation from coarse-to-fine iterations. The proposed adaptive regularization introduces a gradient magnitude penalty function into total variation to preserve and enhance salient edges while smoothing out harmful subtle structures. In addition, the similarity constraint is engaged in each patch without camera rotation effects, ensuring that the erroneous kernels can be identified by measuring the similarity among the kernels of neighbor patches and be replaced with the well-estimated ones. After obtaining accurate kernels, numerous nonblind deblurring methods can be applied to restore an image. Numerical experiments demonstrate that the proposed algorithm performs favorably without ringing artifacts and possesses high processing efficiency for natural nonuniform blurred images.

Single-image deblurring with neural networks: A comparative survey

Neural networks (NNs) are becoming the tool of choice for sharpening blurred images. We discuss and categorize deblurring NNs. Then we evaluate seven NNs for non-blind deblurring (NBD), and seven NNs and four optimization techniques for blind deblurring (BD). To do this we use several current datasets containing pairs of sharp and blurred images, synthesized either by convolving sharp images with blur kernels or by averaging consecutive sharp images, so as to produce both uniform and non-uniform blurs. We also introduce a newly reorganized benchmark dataset in which blurred images have been classified using attributes that depend on the extent of the blur. We use this dataset to compare the effectiveness of single and multi-scale training in coping with large blurs. On NBD, NNs that use regularization with a denoising prior network outperform other denoising NNs; and NNs that use a deep image prior network outperform other deconvolution NNs. On BD, NNs outperform optimizations in signal-difference terms, but not in terms of perceptual fidelity. We found that multi-scale training helps NNs to deal with large blurs, and RNNs outperform CNNs. We also observed that GANs using a perceptual loss function produce artifacts; but also that some form of perceptual fidelity loss is required to get the best results from NNs. We contend that the domain bias of current datasets works against robustness and generality. And we discuss the potential of more sophisticated perceptual loss functions, attention techniques, and unsupervised learning.

Multiple Norms and Boundary Constraint Enforced Image Deblurring via Efficient MCMC Algorithm

Image non-blind deblurring is still an ill-posed problem. Uncertainty in solutions occurs when singular vectors of forward model matrix spanning the noise subspace have rather small singular values. This letter proposes a new image deblurring algorithm, called MNBC-Gibbs (multiple norms and boundary constraint enforced Gibbs sampling). To be more specific, the quadratic and sparseness-inducing norms are combined to construct regularization term, and the objective function is gradually minimized without requirement of regularization parameter choice. In particular, we propose an efficient Markov chain Monte Carlo (MCMC) method equipped with closed-form solution, artifacts processing and non-negative constraint to approximate the posterior distribution and estimate uncertainty for the unknown. Satisfactory deblurring results with sharp edges can be generated while maintaining smoothness without raising extra noise. The quantitative evaluations on different blur kernels and comparison with state-of-the-art image deblurring methods demonstrate the superiority of the proposed method. In addition, we show that our method can effectively deal with real blurry images.

Blind and Non-Blind Deblurring using Residual Whiteness Measures

Blind and Non-Blind Deblurring using Residual Whiteness Measures

Two-stage image deblurring with L0 gradient minimization and non-local refinement

An efficient two-stage non-blind deblurring framework is proposed for recovering blurred images progressively. To this date, most approaches commonly solve a single variational regularization probl...

Cartoon-texture composite regularization based non-blind deblurring method for partly-textured blurred images with Poisson noise

In recent years, deblurring image with Poisson noise has attracted more and more attention in many areas such as astronomy and biological imaging. This is an ill-posed problem and can be regularized to improve the quality of the solution. Fractional-order total variation regularization can eliminate the staircase effect caused by the total variation regularization and avoid over-smoothing at the edges caused by the high-order total variation regularization, but it cannot preserve textures well. Non-local regularization can preserve textures well but often causes extra artifacts especially in the non-texture regions. In this paper, we deal with the cartoon component and the texture component differently and propose a cartoon-texture composite regularization based non-blind deblurring method. We utilize fractional-order total variation regularization for the cartoon component to eliminate the staircase effect and avoid over-smoothing at the edges, and use the non-local total variation regularization for the texture component to preserve the details and eliminate the artifacts in the non-textured regions. We develop an alternating direction method of multipliers based algorithm to solve the proposed model. Experiments results show that our method can improve the result both visually and in terms of the peak signal to noise ratio and structural similarity index efficiently.

Image deblurring associated with shearlet sparsity and weighted anisotropic total variation

Image deblurring is one of the classical problems in image processing. The main purpose of image deblurring is to restore the images corrupted by blur and additive noise while preserving edges and details. We focus on the nonblind image deblurring (NBID) problem. In order to obtain good deblurred images, we propose an NBID method based on a compound regularization model associated with the shearlet-based sparsity and the weighted anisotropic total variation. Based on this model, we present an alternative iterative scheme which consists of the operator splitting and penalty techniques. The split Bregman-based multivariable minimization iteration is introduced to optimize the proposed NBID inverse problem. Compared with some previous methods, the experiments demonstrate the efficiency and viability of the proposed method for preserving sharp edges and structural details of the image while mitigating the artifacts.

A Blind and Non-Blind Deblurring: An Algorithm for Deblurring based on Residual Whiteness in Images

A Blind and Non-Blind Deblurring: An Algorithm for Deblurring based on Residual Whiteness in Images

A Review on Blind and Non-Blind Deblurring Techniques in Image processing

A Review on Blind and Non-Blind Deblurring Techniques in Image processing

Deblurring Shaken and Partially Saturated Images

We address the problem of deblurring images degraded by camera shake blur and saturated (over-exposed) pixels. Saturated pixels violate the common assumption that the image-formation process is linear, and often cause ringing in deblurred outputs. We provide an analysis of ringing in general, and show that in order to prevent ringing, it is insufficient to simply discard saturated pixels. We show that even when saturated pixels are removed, ringing is caused by attempting to estimate the values of latent pixels that are brighter than the sensor’s maximum output. Estimating these latent pixels is likely to cause large errors, and these errors propagate across the rest of the image in the form of ringing. We propose a new deblurring algorithm that locates these error-prone bright pixels in the latent sharp image, and by decoupling them from the remainder of the latent image, greatly reduces ringing. In addition, we propose an approximate forward model for saturated images, which allows us to estimate these error-prone pixels separately without causing artefacts. Results are shown for non-blind deblurring of real photographs containing saturated regions, demonstrating improved deblurred image quality compared to previous work.

Compressed image deblurring

We propose an algorithm to recover the latent image from the blurred and compressed input. In recent years, although many image deblurring algorithms have been proposed, most of the previous methods do not consider the compression effect in blurry images. Actually, it is unavoidable in practice that most of the real-world images are compressed. This compression will introduce a typical kind of noise, blocking artifacts, which do not meet the Gaussian distribution assumed in most existing algorithms. Without properly handling this non-Gaussian noise, the recovered image will suffer severe artifacts. Inspired by the statistic property of compression error, we model the non-Gaussian noise as hyper-Laplacian distribution. Based on this model, an efficient nonblind image deblurring algorithm based on variable splitting technique is proposed to solve the resulting nonconvex minimization problem. Finally, we also address an effective blind image deblurring algorithm which can deal with the compressed and blurred images efficiently. Extensive experiments compared with state-of-the-art nonblind and blind deblurring methods demonstrate the effectiveness of the proposed method. (C) 2014 SPIE and IS&T