Motion Blurred Images Research Articles

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.

Directed color transfer for low-light image enhancement

Underexposed, low-light, images are acquired when scene illumination is insufficient for a given camera. Camera limitation originates in the high chance of producing motion blurred images due to shaky hands. In this paper we suggest to actively use underexposing as a measure to prevent motion blurred images to appear and propose a novel color transfer as a method for low light image amplification. The proposed solution envisages a dual acquisition, containing a normally exposed, possibly blurred image and an underexposed/low-light, but sharp one. Good colors are learned from the normal exposed image and transferred to the low light one using a framework matching solution. To ensure that the transfer is spatially consistent, the images are divided into luminance perceptual consistent patches called frameworks and the optimal mapping is piece-wise approximated. The two image may differ by colors and subject to improve the robustness of the spatial matching, we added supplementary extreme channels. The proposed method shows robust results from both an objective and a subjective point of view.

A Novel Zero-Watermarking Based Scheme for Copyright Protection of Gray scale Images

Zero-watermarking of digital images is a powerful method with respect to transparency in the watermarked image. However, robustness is still a challenging characteristic for researchers. The proposed method of zero-watermarking provides a novel solution for increasing robustness by obtaining resident features of gray scale image that are robust against common signal processing operations. The proposed solution is based on image scanning to produce NDD (Neighboring Distance Difference) profile. This scheme is used to extract image features for generating redundancy binary profile with the help of image scanning and identification of robust image areas for embedding a binary watermark. Redundant areas from binary profile show perceptually insignificant regions of gray scale image according to human visual system. Resident features from robust areas of image are collected to generate the zero watermarking binary key image using reversible XOR operation. The binary key is used for extraction of binary watermark. Experimental results of the proposed method have been compared with the results of various zero-watermarking schemes as well as traditional watermarking methods and found much better at slightly higher computational cost. The comparison analysis for testing robustness has been carried out against image processing attacks like Gaussian filtering, block average filtering, motion blur filtering, image resizing, image rotation, image compression and cropping. For each attack maximum correlated watermark from the set of recovered watermarks is selected to evaluate the performance of proposed zero-watermarking scheme. It has been recorded that perfect matching is observed between original and extracted watermarks for a number of signal processing attacks.

Application of image quality assessment module to motion-blurred wood images for wood species identification system

Despite tighter conservation regulations, demand for timber products has continued to increase due to growing population. Normally, experts identify the wood species based on the pattern of the wood surface texture. However, manual inspection on wood texture is tedious, time-consuming, impractical and cost-ineffective for a human to analyze a large number of timber species. Therefore, a reliable automatic wood recognition system is needed in order to classify the wood species efficiently. The proposed system includes image acquisition, image quality assessment module (IQA), image deblurring, feature extraction and classification. In this research, the wood images are motion-blurred due to imperfections in the imaging and capturing process. Hence, an IQA module is proposed to monitor the quality of images before proceeding to the next stage which is the feature extraction process. The IQA module will determine whether the image has to undergo the image deblurring process based on the image quality value. If the image is of low quality based on the image quality value obtained, then the image will be deblurred before the feature extraction procedure. A reliable motion deblurring technique, which is based on Lucy–Richardson algorithm, is employed to enhance the motion-blurred images before proceeding to the next stage, which is the feature extraction process. Then, a statistical feature extraction technique is proposed to extract 24 features from each wood image. Finally, a support vector machine is used to classify the 20 tropical wood species.

Measuring shape and motion of a high-speed object with designed features from motion blurred images

Vision-based geometry measurement plays a crucial role in many science and industrial areas. Plenty of researches devoted to measuring static objects, while few focused on motion blurred situations, which inevitably arise when the object being measured moves fast relative to the camera(s). Motion blur usually invalids the vision-based measurement algorithms designated for static objects. In this paper, we devote to accurate three dimensional (3D) reconstruction of moving objects from motion blurred stereo image pairs. A convolutional neural network (CNN) based method is first proposed to recognize the motion blurred visual targets. A motion blur model based on inner-frame path superposition imaging is then established. Finally, an optimization framework is set up to reconstruct the 3D target motion path during the camera exposure. Experiments are involved to demonstrate the validity and accuracy of the method.

Quick response barcode deblurring via L 0 regularisation based sparse optimisation

In recent years, the two-dimensional barcode techniques have been widely applied in a large number of fields due to the requirement for the development of information acquisition technology. Also, the identification and verification of the static barcodes could be implemented flawlessly. However, the recognition of the motion blurred two-dimensional barcode images still remains a challenge to most of the current machine vision systems. To address the above-mentioned problem, the authors propose a regularisation-based approach to reverse the blurry barcode images while taking the number of black and white blocks in the two-dimensional barcode image as the prior. To evaluate the performance of the proposed approach, they conducted comparison experiments between state-of-the-art image deblurring methods and ours. Experimental results on synthetically blurry barcode images show that the proposed method outperforms the state-of-the-art image restoration techniques.

Blind motion image deblurring using an effective blur kernel prior

Blind image deblurring, i.e., reconstructing a sharp version of a blurred image, is generally an ill-posed problem, as both the blur kernel and the sharp image are unknown. To solve such problem, one must use effective image and blur kernel priors. In this paper, a blind image deblurring method is proposed, which uses an effective image prior based on both the first and second order gradients of the image. This prior causes to properly reconstruct salient edges which provide reliable edge information for kernel estimation in the intermediate latent image. This prior along with a hyper-Laplacian kernel prior can be used to solve the optimization problem in the form of maximum-a posteriori-problem, and hence obtaine the blur kernel with a high accuracy. The efficiency of the proposed method is demonstrated by performing several quantitative and qualitative comparisons with the state-of-the-art methods, on both a benchmark image dataset and real-world motion blurred images.

Weighted sparse representation multi-scale transform fusion algorithm for high dynamic range imaging with a low-light dual-channel camera.

Most imaging devices lose image information during the acquisition process due to their low dynamic range (LDR). Existing high dynamic range (HDR) imaging techniques have a trade-off with time or spatial resolution, resulting in potential motion blur or image misalignment. Current HDR methods are based on the fusion of multi-frame LDR images and can suffer from blurring of fine details, image aliasing, and image boundary effects. This study developed a dual-channel camera (DCC) to achieve HDR imaging, which can eliminate image motion blur and registration problems. Considering the output characteristics of the camera, we propose a weighted sparse representation multi-scale transform fusion algorithm, which fully preserves the original image information, while eliminating image aliasing and boundary problems in the fused image, resulting in high-quality HDR imaging.

Depth-Aware Motion Deblurring Using Loopy Belief Propagation

Most motion-blurred images captured in the real world have spatially-varying point-spread functions, and some are caused by different positions and depth values, which cannot be handled by most state-of-the-art deblurring methods based on deconvolution. To overcome this problem, we propose a depth-aware motion blur model that treats a blurred image as an integration of a sequence of clear images. To restore the clear latent image, we extend the Richardson-Lucy method to incorporate our blur model with a given depth image. The empty holes in the depth image, caused by occlusion or device limitations, are fixed by PatchMatch-based depth filling. We regard the depth image as a Markov random field and select candidate labels by using belief propagation to set and smooth depth values for empty areas. Deblurring and depth filling are performed iteratively to refine the results. Our method can also be applied to real-world images with the assistance of motion estimation. The deblurring process is shown to be convergent; moreover, the number of iterations and the level of noise amplification are acceptable. The experimental results show that our method can not only handle depth-variant motion blur but also refine depth images.

Research on de-motion blur image processing based on deep learning

In recent years, with the rapid development of computer technology and network technology, computer vision has been widely used in various scientific fields. Human motion recognition, as an important branch of computer vision, is essentially to classify human motion information in motion images correctly. It has great significance in intelligent monitoring and security, human-computer interaction, motion analysis and other fields. At present, there are still some problems in human motion recognition methods. Firstly, how to extract and characterize the motion information in images has been one of the difficulties in this field; secondly, with the appearance of kinect and other depth cameras, researchers have provided the depth information of human motion images, and how to effectively use these depth information to achieve human motion recognition and classification is also an important research issue; finally, when the amount of sample data is small, how to use the deep learning network model to achieve a higher human motion recognition rate? Based on UTD-MHAD database, this paper studies the human motion recognition of RGB image and depth image captured simultaneously by kinect, and carries out relevant discussion and analysis on the above problems, using micro-inertial sensors (MTi-G-700 developed by Xsens and Android mobile phones, tablets and other personal mobile devices come with MEMS gyroscopes and accelerometers) to correct the image to motion blur, build a new mathematical model, use the inertial data obtained by MIMU in a short time to estimate the position, attitude and speed of camera motion, correct the image pixel position, perform image de-motion blur processing, and then perform image processing such as denoising to solve the image motion blur problem. A new algorithm is developed and its science is verified by MATLAB simulation.

Motion blur image deblurring using edge-based color patches

The shaking of a camera can easily cause blurs in an image. Thus, deblurring is a problem that is worth solving and has always been an active research interest. The color information in an image is an important feature and contains clues for image deblurring that have not been widely exploited. In this paper, we present an efficient and stable blurring kernel estimation method by solving an energy function constructed by a weighted color approximation regularization term. The term is derived from a two-color model, and we use a defined weight to alleviate the color change through the blurring process. Then we select salient edges in an effective way to apply the proposed method on the patches centered at these edges. Experiments on synthetic and real-world images show the efficiency and stability of our proposed method.

Radon transform-based motion blurred silkworm pupa image restoration

Radon transform-based motion blurred silkworm pupa image restoration

Multi-Regularization-Constrained Blur Kernel Estimation Method for Blind Motion Deblurring

Blur kernel (BK) estimation is the crucial technique to guarantee the success of blind image deblurring. In this paper, we propose a multi-regularization-constrained method to estimate an accurate BK from a single motion-blurred image. First, in order to generate sharp and reliable intermediate latent results, we propose a model which combines the spatial scale, ${L} _{0}$ norm, and the dark channel prior. Second, in order to preserve the continuity and the sparsity, and to remove the flaw in the BK, a dual-constrained regularization model, which combines the ${L} _{0}$ -regularized intensity prior and the ${L} _{2}$ -regularized gradient prior, is proposed for accurate BK estimation. The proposed model can not only preserve the continuity and the sparsity of the BK very well but also can remove the flaw thoroughly. Finally, we propose an efficient optimization strategy which can solve the proposed model efficiently. Extensive experiments compared with the state-of-the-art methods demonstrate that our method estimates more accurate BKs and obtains higher quality deblurring images in terms of both subjective vision and quantitative metrics.

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

Optimization-based legged odometry and sensor fusion for legged robot continuous localization

In this manuscript, we tackle the problem of a continuous localization of a legged robot. We propose a novel, optimization-based procedure for the state estimation of the robot using measurements from internal sensors (legged odometry). Then, we propose the optimization-based integration of the legged odometry and the visual SLAM output. The proposed multi-modal localization system can continuously estimate the pose of the robot in various conditions despite fast motions of the robot, slippages or image motion blur. We provide the results of the real-time implementation of the proposed method on a multi-legged walking robot. We compare the proposed localization method to other state of the art localization systems and provide the dataset for future comparisons.

Blind color-image deblurring based on color image gradients

In this paper, a blind color-image deblurring method based on color image gradients is proposed. The color-image deblurring model is built by introducing color image gradients in the rgb normalized space. The deblurring process is based on alternating iterations of image estimations and blurring kernel estimations. The clear image is obtained by the alternating direction method of multipliers (ADMM), and the blurring kernel is calculated by the half-quadratic penalty method. Experimental results show that the proposed method effectively estimates the blurring kernels. The fidelity of the deblurred images is better, for both motion blurred images and defocused images, than what is achieved with other state-of-the-art blind image deblurring methods. Furthermore, the quality metrics for the proposed method also verify the value of our method compared with other methods.

Fast detection and segmentation of partial image blur based on discrete Walsh–Hadamard transform

Image signals can be blurred due to defocus or motion. Blur may be undesirable for image sensing, but may also contain useful information. Therefore, detecting the blurriness of each pixel and segmenting the partial blur regions in natural images are important and yet challenging in the field of machine vision. A concise no-reference method based on discrete Walsh–Hadamard transform is proposed to detect and segment partial blur in this paper. First, a re-blurring strategy is performed over multiple overlapping image patches extracted from the test image. Then, for both test image and re-blurred image, discrete Walsh–Hadamard transforms are utilized in each image patches to obtain the blur map. This blur map can characterize the blurriness of each pixel in test image. Based on it, combined with K-Means clustering and region-growing, the test image can be segmented into blurry/non-blurry regions. The experiments, performed on a public dataset, demonstrate the capability of the proposed metric in the detection and segmentation of the blur region. Comparative results with the state-of-the-art show the superiority of the proposed approach in image segmentation for both defocus and motion blur images. The proposed approach is compendious without data training and possesses a high time efficiency because of the fast sequency transform.

Motion Blur Image Restoration Algorithm Based on the Theory of Reaction Diffusion

Image restoration is of great significance in the digital image processing. When imaging equipment obtains the image, if the imaging equipment and the scene occur relative motion in the exposure time, it could lead to actually get blurred image. Due to motion blur, the got image loses real value. So the research of motion blur image restoration has important application value. This paper proposes a motion blur image restoration algorithm based on the theory of reaction diffusion. The algorithm uses the reaction diffusion equations related mathematical knowledge as the background, build the motion blur image restoration model and design motion blur image restoration algorithm. Through the experiment, the algorithm compares with the common motion blur restoration algorithm (RI-BM3D, BM3DDEB, IDD-BM3D and ForWaRD). The subjective and objective restoration results of the proposed image restoration algorithm are obviously improved.

Age Estimation Robust to Optical and Motion Blurring by Deep Residual CNN

Recently, real-time human age estimation based on facial images has been applied in various areas. Underneath this phenomenon lies an awareness that age estimation plays an important role in applying big data to target marketing for age groups, product demand surveys, consumer trend analysis, etc. However, in a real-world environment, various optical and motion blurring effects can occur. Such effects usually cause a problem in fully capturing facial features such as wrinkles, which are essential to age estimation, thereby degrading accuracy. Most of the previous studies on age estimation were conducted for input images almost free from blurring effect. To overcome this limitation, we propose the use of a deep ResNet-152 convolutional neural network for age estimation, which is robust to various optical and motion blurring effects of visible light camera sensors. We performed experiments with various optical and motion blurred images created from the park aging mind laboratory (PAL) and craniofacial longitudinal morphological face database (MORPH) databases, which are publicly available. According to the results, the proposed method exhibited better age estimation performance than the previous methods.

Maximizing autonomous performance of fixed-wing unmanned aerial vehicle to reduce motion blur in taken images

In this study, reducing motion blur in images taken by our unmanned aerial vehicle is investigated. Since shakes of unmanned aerial vehicle cause motion blur in taken images, autonomous performance of our unmanned aerial vehicle is maximized to prevent it from shakes. In order to maximize autonomous performance of unmanned aerial vehicle (i.e. to reduce motion blur), initially, camera mounted unmanned aerial vehicle dynamics are obtained. Then, optimum location of unmanned aerial vehicle camera is estimated by considering unmanned aerial vehicle dynamics and autopilot parameters. After improving unmanned aerial vehicle by optimum camera location, dynamics and controller parameters, it is called as improved autonomous controlled unmanned aerial vehicle. Also, unmanned aerial vehicle with camera fixed at the closest point to center of gravity is called as standard autonomous controlled unmanned aerial vehicle. Both improved autonomous controlled and standard autonomous controlled unmanned aerial vehicles are performed in real time flights, and approximately same trajectories are tracked. In order to compare performance of improved autonomous controlled and standard autonomous controlled unmanned aerial vehicles in reducing motion blur, a motion blur kernel model which is derived using recorded roll, pitch and yaw angles of unmanned aerial vehicle is improved. Finally, taken images are simulated to examine effect of unmanned aerial vehicle shakes. In comparison with standard autonomous controlled flight, important improvements on reducing motion blur are demonstrated by improved autonomous controlled unmanned aerial vehicle.