Blur Parameters Research Articles

Sub-resolution modeling of the apparent mass loss in quantitative broadband X-ray radiography

The quantitative impact of image blur on calculated object mass in X-ray radiography is explored. Radiographed object masses are initially estimated through experimental calibration and compared against their known true mass, with the deviation, or “apparent mass loss”. Apparent mass loss occurs due to the effect of spatial blur on X-ray transmission images that reduces the measured mass because while the image intensity is conserved with spatial blur, the nonlinear conversion to path length is not. A synthetic model is proposed where an object’s apparent mass loss is estimated through the amount of blur present within the radiograph image. The image blur is inferred through a combination of readily available image parameters related to the signal levels, object shape, and experimental extrinsics. A regularized regression model is built that correlates these blur parameters to the expected mass loss. The model is first trained on 2100 and tested on 140 synthetically created objects, and then later verified on two separate experimental setups of type 304 stainless steel objects imaged with a portable tube source at 150 kVp and type 6061 aluminum objects at 80 kVp. The proposed model allows for a simple post hoc reduction in errors of approximately 20% in object mass through X-ray radiography.

Improving visual quality of blurred digital images

The goal of image deblurring is to recover a sharp image with the necessary edge structure and sharp details from the input blurred image. Blurring occurs due to various reasons, such as motion or focal length, camera shake, subject movement, or out of focus. Since, in practice, the blur parameters are unknown or known only approximately by the nature of the visible distortions, this study discusses methods for eliminating image blur blindly. The article explores the latest blind blur removal algorithms developed in the last five years: SVRNN, SRN, RCGAN, Gao et al., DMPHN, DeblurGAN, MBMF, MSCNN, DeblurGAN-v2, EDPN, RCAN, MSRN, GFN, SelfDeblur. The task of the study is to choose the most effective method for removing blur, for further application in the development of software to improve the visual qualities of digital images. Experiments have shown that the newly developed methods converge faster than older algorithms and are able to extract an image that is more consistent with the subjective opinion of experts. DeblurGAN-v2, EDPN and DBGAN(+) are currently the best method for restoring blurry images.

Motion-blurred image restoration framework based on parameter estimation and fuzzy radial basis function neural networks

The restoration of motion-blurred images has always been a complex problem in image restoration. The current single blurred image algorithm cannot very well solve the estimation error of motion blur parameters. A comprehensive motion-blurred image restoration framework is proposed, which includes motion-blurred data generation, blur parameter estimation, and image quality assessment of restored images. First, we designed and used four image data sets with different degrees of blurring. We innovatively propose a blur parameter estimation algorithm based on the particle swarm optimization (B-PSO) algorithm. The Naturalness Image Quality Evaluator (NIQE) is used as the fitness function of the PSO algorithm. The framework also introduces a polynomial-based radial basis function neural network (P-RBFNN) as a new image quality assessment (IQA) method, with good image classification performance. Test results from public datasets show that the proposed framework can accurately estimate blur parameters. The peak signal-to-noise ratio (PSNR) reaches 29.976 dB, the structural similarity (SSIM) reaches 0.9044, and the classification rate is 96%. The proposed restoration framework produces the best image restoration results.

Efficient Image Deblurring Application Using Combination of Blind Deconvolution Method and Blur Parameters Estimation Method

Image deblurring can be formulated as the inverse process of image blurring. The degraded image can be improved and reconstructed to its original form using image deblurring. Image reconstruction in elementary mode begins with estimation of degradation parameters followed with application of some classical restoration approach. This paper introduces a new approach for image restoration. Here, removal of motion blurs is done through blind deconvolution technique. Two motion parameters angle (θ) and length have been considered for estimation. Hough transform method is used for estimation of blur parameters. PSNR and MSE are employed to exploit important image characteristics, such as denoising, error, randomness, etc. Proposed algorithm is tested for accuracy. Experiments are carried out to study important image features, which play a major role during image restoration.

Motion Blur Fuzzy Blind Removal Algorithm for Character Images in Gradient Domain and Deep Learning

The imaging device is susceptible to factors such as the subject or the shooting environment when imaging, and complex variable blurring occurs in the final imaging. In most cases, we not only do not have the conditions to re-shoot a clear image but also do not know the specific parameters of the variable blur in advance. Therefore, the purpose of this study is to propose a motion blur fuzzy blind removal algorithm for character images based on gradient domain and depth learning. Deep learning is to learn the inherent laws and representation levels of sample data, and the information obtained during these learning processes is of great help to the interpretation of data such as text, images, and sounds. The algorithm used in this study is to preprocess the image by using guided filtering and L0 filtering and send the preprocessed gradient domain image block to the designed convolutional neural network for training. Extract the trained model parameters and realize the fuzzy kernel estimation and image. Image deblurring is performed using the TV regular term during image restoration. The experiment proves that the algorithm can effectively suppress the ringing effect and reduce the noise, and the motion blur effect is better. In this study, the MLP method, the edge detection method, and the proposed method are discussed, respectively. The PSNR values of the three motion blur removal methods are 26.49, 27.51, and 29.18, respectively. It can be seen that the motion blur removal method proposed in this study can effectively remove image motion blur.

Estimation and Sharpening of Blur in Degraded Images Captured by a Camera on a Moving Object

In this research, we aim to propose an image sharpening method to make it easy to identify concrete cracks from blurred images captured by a moving camera. This study is expected to help realize social infrastructure maintenance using a wide range of robotic technologies, and to solve the future labor shortage and shortage of engineers. In this paper, a method to estimate parameters of motion blur for Point Spread Function (PSF) is mainly discussed, where we assume that there are two main degradation factors caused by the camera, out-of-focus blur and motion blur. A major contribution of this paper is that the parameters can properly be estimated from a sub-image of the object under inspection if the sub-image contains uniform speckled texture. Here, the cepstrum of the sub-image is fully utilized. Then, a filter convoluted PSF which consists of convolution with PSF (motion blur) and PSF (out-of focus blur) can be utilized for deconvolution of the blurred image for sharpening with significant effect. PSF (out-of-focus blur) is a constant function unique to each camera and lens, and can be confirmed before or after shooting. PSF (motion blur), on the other hand, needs to be estimated on a case-by-case basis since the amount and direction of camera movement varies depending on the time of shooting. Previous research papers have sometimes encountered difficulties in estimating the parameters of motion blur because of the emphasis on generality. In this paper, the main object is made of concrete, and on the surface of it there are speckled textures. We hypothesized that we can narrow down the candidates of parameters of motion blur by using these speckled patterns. To verify this hypothesis, we conducted experiments to confirm and examine the following two points using a general-purpose camera used in actual bridge inspections: 1. Influence on the cepstrum when the isolated point-like texture unique to concrete structures is used as a feature point. 2. Selection method of multiple images to narrow down the candidate minima of the cepstrum. It is novel that the parameters of motion blur can be well estimated by using the unique speckled pattern on the surface of the object.

Using a Blur Metric to Estimate Linear Motion Blur Parameters.

Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blurry, this method can be used to estimate the blur parameters and then take an action to enhance the image. In this paper, some methods are proposed for estimating the linear motion blur parameters based on the extraction of features from the given single blurred image. The motion blur direction is estimated using the Radon transform of the spectrum of the blurred image. To estimate the motion blur length, the relation between a blur metric, called NIDCT (Noise-Immune Discrete Cosine Transform-based), and the motion blur length is applied. Experiments performed in this study showed that the NIDCT blur metric and the blur length have a monotonic relation. Indeed, an increase in blur length leads to increase in the blurriness value estimated via the NIDCT blur metric. This relation is applied to estimate the motion blur. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.

A self-adaptive learning method for motion blur kernel estimation of the single image

The estimation of blur kernel is the first and principal steps in the deconvolution of single blurred image. The quality of image restoration highly depends on its estimation accuracy. We then propose a new modified-Radon-transform approach along with a low-high-pass filtering method to estimate the motion blur parameters by a self-adaptive learning strategy, which greatly improved the deblurring quality of the blurred image. The Gaussian low-pass and high-pass filters are adopted to reduce the noise level in blurred image, and the batch normalization and self-adaptive method are considered to eliminate the interference from the noise stripes. It is noted that the estimation of blur angle plays an important cue for the exploration of blur kernel. The experimental evaluation is conducted on both synthetic VOC2012 database as well as the natural-real motion blurred single image with or without noise. The experimental results show that our proposed method can obtain more accurate and more reliable blur parameters than other approaches.

Research on Fast Estimation Method of Fuzzy Parameters for Motion Blurred Images

Motion blur distortion is the most common type of image distortion in daily life. the research on motion-blurred image restoration technology has developed more mature. Classical algorithms such as Wiener filter and Kalman filter and various improved algorithms can achieve better results, but they take a long time and have great limitations in actual image restoration application scenarios. To solve this problem, this paper proposes an algorithm for fast restoration of image motion blur, an improved algorithm based on Randon transform to judge the image motion blur angle, and studies the algorithm for estimating the blur length under the condition that the blur angle is determined, and obtains two motion blur parameters of blurred image. Experimental results show that the fast image restoration method proposed in this paper has shorter time consumption, stronger anti-noise interference ability and better practicability.

Identifikasi Karakteristik Citra Berdasarkan pada Nilai Entropi dan Kontras

Abstract 
 Determining the object boundaries in an image is a necessary process, to identify the boundaries of an object with other objects as well as to define an object in the image. The acquired image is not always in good condition, on the other hand there is a lot of noise and blur. Various edge detection methods have been developed by providing noise parameters to reduce noise, and adding a blur parameter but because these parameters apply to the entire image, but lossing some edges due to these parameters. This study aims to identify the characteristics of the image region, whether the region condition is noise, blurry or otherwise sharp (clear). The step is done by dividing the four regions from the image size, then calculating the entropy value and contrast value of each formed region. The test results show that changes in region size can produce different characteristics, this is indicated by entropy and contrast values ​​of each formed region. Thus it can be concluded that entropy and contrast can be used as a way to identify image characteristics, and dividing the image into regions provides more detailed image characteristics.

Improved estimation of motion blur parameters for restoration from a single image.

This paper presents an improved method to estimate the blur parameters of motion deblurring algorithm for single image restoration based on the point spread function (PSF) in frequency spectrum. We then introduce a modification to the Radon transform in the blur angle estimation scheme with our proposed difference value vs angle curve. Subsequently, the auto-correlation matrix is employed to estimate the blur angle by measuring the distance between the conjugated-correlated troughs. Finally, we evaluate the accuracy, robustness and time efficiency of our proposed method with the existing algorithms on the public benchmarks and the natural real motion blurred images. The experimental results demonstrate that the proposed PSF estimation scheme not only could obtain a higher accuracy for the blur angle and blur length, but also demonstrate stronger robustness and higher time efficiency under different circumstances.

A new approach to enhance the estimation of blur parameters in blurred images

One of the main objectives of blur parameters estimation is the restoration of blurred images. A novel blur parameters estimation algorithm is presented in the present paper. For estimating the blur angle of the degraded image, Hough transform is applied upon two parallel linear edges on two sides of the central bright stripe that appear in the frequency domain. For blur length estimation, the proposed method has two sequential phases. In the first phase, the frequency domain is used for feature extraction. Also, for obtaining a superior performance in the algorithm in the second stage, a wavelet-based parameter was used, which can determine the extent of the blur in the image. For the prediction of the blur length in a particular blurred image, RBFNN is employed with frequency-domain features of the related blurred image and blur extent parameter, coming from the wavelet transform as the input. Blur length in blurred images is estimated at a higher accuracy using this method. The concentration of this approach is on motion blur parameter estimation. Through comparison with available approaches, the results of simulations indicate the ability and strength of the suggested approach in predicting blur parameters.

RETRACTED ARTICLE: Conventional neural network for blind image blur correction using latent semantics

In this work, deep learning for enhancing the sharpness of blurred image is investigated. Initial pre-processing is blur image kernel estimation which is critical for blind image de-blurring. In prior investigation, handcrafted blur features are optimized for certain uniform blur, which is unrealistic for blind de-convolution. To deal with this crisis, initially this work attempts to carry out kernel matrix estimation using latent semantic analysis (KME-LSA) in dermatology image. In order to enhance the image sparseness, this work modelled an image descriptor based on Gaussian mixture model in auto-encoder (GMM-AE) as a primary layer in convolutional neural networks. The functionality of the proposed GMM-AE triggers the selection of efficient features for subsequent layers in CNN. The features extracted from the integrated trained GMM-AE in CNN can fine-tune the quality of blurred image. Datasets used are melanoma-based dermascope images. Pre-processing procedures are carried out by LSA-based kernel matrix estimation. The attained sharp image outcome is given to the proposed model for effective feature extraction and to attain improved blind image. The anticipated KME-LSA and GMM-AE in CNN estimates blur parameters with high accuracy. Experiment illustrates the efficacy of proposed method and the competitive outcomes are compared with state-of-the-art datasets. Simulation was carried out in MATLAB environment; performance metrics like MSE—227.6, PSNR—33.6762, SSIM—0.9755 and VIF—0.08162 are evaluated. The results show better trade-off than the prevailing techniques.

Blur parameter locus curve and its applications

Conventionally, the point spread function (PSF) is understood as a characteristic function of any optical system. It captures the information about the amount of blur present along all the directions for a point in the scene. However, the dependence of blur on the PSF is in the form of convolution for any object other than a point source present in the scene and hence their relationship is less explicit. The authors propose a blur parameter locus curve (BPLC) as a system representation which has a one to one relationship with blur. BPLC simply is a chart of blur amounts in all directions of a given PSF with respect to the selected measurement function. They further characterise the PSF by decomposing the variation of BPLC across all directions based on the study performed for different possible forms of the blur kernels. Such decomposition provides powerful tools for various analysis. As PSF can be anisotropic, the computation of BPLC becomes an essential intermediate step to obtain the scale map as at the same scale, blur is different in different directions. Furthermore, they demonstrate the use of BPLC to obtain other system characteristics function such as PSF.

A no-reference respiratory blur estimation index in nuclear medicine for image quality assessment.

Few indexes are available for nuclear medicine image quality assessment, particularly for respiratory blur assessment. A variety of methods for the identification of blur parameters has been proposed in literature mostly for photographic pictures but these methods suffer from a high sensitivity to noise, making them unsuitable to evaluate nuclear medicine images. In this paper, we aim to calibrate and test a new blur index to assess image quality.Blur index calibration was evaluated by numerical simulation for various lesions size and intensity of uptake. Calibrated blur index was then tested on gamma-camera phantom acquisitions, PET phantom acquisitions and real-patient PET images and compared to human visual evaluation.For an optimal filter parameter of 9, non-weighted and weighted blur index led to an automated classification close to the human one in phantom experiments and identified each time the sharpest image in all the 40 datasets of 4 images. Weighted blur index was significantly correlated to human classification (ρ = 0.69 [0.45;0.84] P < .001) when used on patient PET acquisitions.The provided index allows to objectively characterize the respiratory blur in nuclear medicine acquisition, whether in planar or tomographic images and might be useful in respiratory gating applications.

Blind deconvolution in model-based iterative reconstruction for CT using a normalized sparsity measure

Model-based iterative reconstruction techniques for CT that include a description of the noise statistics and a physical forward model of the image formation process have proven to increase image quality for many applications. Specifically, including models of the system blur into the physical forward model and thus implicitly performing a deconvolution of the projections during tomographic reconstruction, could demonstrate distinct improvements, especially in terms of resolution. However, the results strongly rely on an exact characterization of all components contributing to the system blur. Such characterizations can be laborious and even a slight mismatch can diminish image quality significantly. Therefore, we introduce a novel objective function, which enables us to jointly estimate system blur parameters during tomographic reconstruction. Conventional objective functions are biased in terms of blur and can yield lowest cost to blurred reconstructions with low noise levels. A key feature of our objective function is a new normalized sparsity measure for CT based on total-variation regularization, constructed to be less biased in terms of blur. We outline a solving strategy for jointly recovering low-dimensional blur parameters during tomographic reconstruction. We perform an extensive simulation study, evaluating the performance of the regularization and the dependency of the different parts of the objective function on the blur parameters. Scenarios with different regularization strengths and system blurs are investigated, demonstrating that we can recover the blur parameter used for the simulations. The proposed strategy is validated and the dependency of the objective function with the number of iterations is analyzed. Finally, our approach is experimentally validated on test-bench data of a human wrist phantom, where the estimated blur parameter coincides well with visual inspection. Our findings are not restricted to attenuation-based CT and may facilitate the recovery of more complex imaging model parameters.

New Cepstrum Based Image Restoration Algorithm for Grayscale Images

Optimization is the process that relates to finding the most excellent ways for all possible solutions. From last 2-3 decades, natural algorithms play an important role in improving solutions of various problems. By comparing various meta-heuristic algorithms, researchers can make a choice to the best selection of the meta-heuristic algorithms for the proposed problem. In this particular research, we have applied New Cepstrum based technique of image restoration to find out PSF parameters of motion blurred images as a primary technique. In addition, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), BAT Algorithm and GA-BAT hybrid technique etc. are also applied to optimize the blur parameters for calculated by new cepstrum based technique for blur estimation. This aids in analyzing the performance of each algorithm on the same primary technique. The performance analysis of all four algorithms aid in making the decision on the best meta-heuristic algorithm of the cepstrum based technique and to identify the preciseness of the motion blur. All four methods are applied to the same set of images. The algorithm is tested and compared using grayscale images and the benchmarking freely available online datasets, respectively.

A Blur-SURE-Based Approach to Kernel Estimation for Motion Deblurring

Blind motion deblurring is a highly challenging inverse problem in image processing and low-level computer vision. In this paper, we propose a novel approach to identify the parameters (blur length and orientation) of motion blur from an observed image. The kernel estimation is based on a novel criterion — the minimization of a blurred Stein’s unbiased risk estimate (blur-SURE): an unbiased estimate of a filtered mean squared error. By incorporating a simple Wiener filtering into the blur-SURE, the motion blur is estimated by minimizing this new objective functional with high accuracy. We then perform non-blind deconvolution using the high-quality SURE-LET algorithm with the estimated kernel. The results of synthetic and real experiments are quite competitive with other state-of-the-art algorithms under a wide range of degradation scenarios both numerically and visually.

Velocity Detection from a Motion Blur Image Using Radon Transformation

Motion blur is the result when the camera shutter remains open for an extended period of time and a relative motion between camera and object occurs. An approach for velocity detection based on motion blurred images has been implemented by the Radon transformation. The motion blur parameters are first estimated from the acquired images by using Radon transformation and then used to detect the speed of the moving object in the scene. Here established a link between the motion blur information of a 2D image and camera manufacturer’s data sheet and its calibration

PARAMETRIC BLIND IMAGE DEBLURRING WITH GRADIENT BASED SPECTRAL KURTOSIS MAXIMIZATION

Blind image deconvolution/deblurring (BID) is a challenging task due to lack of prior information about the blurring process and image. Noise and ringing artefacts resulted during the restoration process further deter fine restoration of the pristine image. These artefacts mainly arise from using a poorly estimated point spread function (PSF) combined with an ineffective restoration filter. This paper presents a BID scheme based on the steepest descent in kurtosis maximization. Assuming uniform blur, the PSF can be modelled by a parametric form. The scheme tries to estimate the blur parameters by maximizing kurtosis of the deblurred image. The scheme is devised to handle any type of blur that can be framed into a parametric form such as Gaussian, motion and out-of-focus. Gradients for the blur parameters are computed and optimized in the direction of increasing kurtosis value using a steepest descent scheme. The algorithms for several common blurs are derived and the effectiveness has been corroborated through a set of experiments. Validation has also been carried out on various real examples. It is shown that the scheme optimizes on the parameters in a close vicinity of the true parameters. Results of both benchmark and real images are presented. Both full-reference and non-reference image quality measures have been used in quantifying the deblurring performance. The results show that the proposed method offers marked improvements over the existing methods.