Restoration Of Motion Blurred Images Research Articles

A blurred star image restoration method based on gyroscope data and enhanced sparse model

Abstract Star sensors usually have a fixed exposure time to guarantee detection of adequate navigation stars. In a high dynamic environment, star images suffer from degradation due to spacecraft movement, which will severely affects both centroid extraction and attitude accuracy. This paper presents an algorithm for the restoration of motion-blurred star images. The algorithm employs gyroscope assistance and consists of two steps: preprocessing and motion-blurred image restoration. In the preprocessing step, the angular velocity of the gyroscope predicts the motion trajectory, position, and shape of each star point during exposure. This step ensures a good initial estimate of the blur kernel for image restoration. The image restoration step employs an enhanced sparse model inspired by blind deblurring method to solve blur kernel and latent image alternately. Simulations and experiments have verified the effectiveness of the proposed algorithm.

Prior information-based motion blur image restoration method for the low-frequency sinusoidal vibration measurements with machine vision

The sinusoidal vibration is the basic unit of motion control and measurement, which can constitute the arbitrary plane and space motions and is essential to the low-frequency vibration calibration. At present, the machine vision (MV) method has been widely applied to measure the low-frequency vibration, which can promote the improvement of low-frequency vibration calibration and metrology traceability. However, the MV method is always inevitably affected by the motion blur which decreases its measurement accuracy. In this study, a novel prior information-based motion blur image restoration method is investigated, which applies the known sinusoidal information and the Lucy-Richardson (LR) method with Non-Local (NL)-Means to eliminate the motion blur and improve the measurement accuracy. Meanwhile, the objective evaluation indexes and edge detection method are adopted to verify the restoration effect. Several simulations and actual experiment show that the investigated method can significantly improve the quality of the sinusoidal motion blur images, and further make the accuracies of the edge detection method and MV method increase 0.3 pixel and 0.3%, respectively.

WRA-Net: Wide Receptive Field Attention Network for Motion Deblurring in Crop and Weed Image.

Automatically segmenting crops and weeds in the image input from cameras accurately is essential in various agricultural technology fields, such as herbicide spraying by farming robots based on crop and weed segmentation information. However, crop and weed images taken with a camera have motion blur due to various causes (e.g., vibration or shaking of a camera on farming robots, shaking of crops and weeds), which reduces the accuracy of crop and weed segmentation. Therefore, robust crop and weed segmentation for motion-blurred images is essential. However, previous crop and weed segmentation studies were performed without considering motion-blurred images. To solve this problem, this study proposed a new motion-blur image restoration method based on a wide receptive field attention network (WRA-Net), based on which we investigated improving crop and weed segmentation accuracy in motion-blurred images. WRA-Net comprises a main block called a lite wide receptive field attention residual block, which comprises modified depthwise separable convolutional blocks, an attention gate, and a learnable skip connection. We conducted experiments using the proposed method with 3 open databases: BoniRob, crop/weed field image, and rice seedling and weed datasets. According to the results, the crop and weed segmentation accuracy based on mean intersection over union was 0.7444, 0.7741, and 0.7149, respectively, demonstrating that this method outperformed the state-of-the-art methods.

Severe motion blurred silkworm pupae image restoration in sex discrimination

Severe motion blurred silkworm pupae image restoration in sex discrimination

RMOBF-Net: Network for the Restoration of Motion and Optical Blurred Finger-Vein Images for Improving Recognition Accuracy

Biometrics is a method of recognizing a person based on one or more unique physical and behavioral characteristics. Since each person has a different structure and shape, it is highly secure and more convenient than the existing security system. Among various biometric authentication methods, finger-vein recognition has advantages in that it is difficult to forge because a finger-vein exists inside one’s finger and high user convenience because it uses a non-invasive device. However, motion and optical blur may occur for some reasons such as finger movement and camera defocusing during finger-vein recognition, and such blurring occurrences may increase finger-vein recognition error. However, there has been no research on finger-vein recognition considering both motion and optical blur. Therefore, in this study, we propose a new method for increasing finger-vein recognition accuracy based on a network for the restoration of motion and optical blurring in a finger-vein image (RMOBF-Net). Our proposed network continuously maintains features that can be utilized during motion and optical blur restoration by actively using residual blocks and feature concatenation. Also, the architecture RMOBF-Net is optimized to the finger-vein image domain. Experimental results are based on two open datasets, the Shandong University homologous multi-modal traits finger-vein database and the Hong Kong Polytechnic University finger-image database version 1, from which equal error rates of finger-vein recognition accuracy of 4.290–5.779% and 2.465–6.663% were obtained, respectively. Higher performance was obtained from the proposed method compared with that of state-of-the-art methods.

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.

An optimization method for motion blur image restoration and ringing suppression via texture mapping

Since the image sensor will produce blur problems in the process of collecting data of moving objects, the image needs to be restored. Ringing is one of the most common artifacts in deblurred images. This paper proposes a non-blind image deconvolution method based on texture mapping segmentation, named texture-Richardson–Lucy (TRL) algorithm, which suppresses ringing while deblurring the image. TRL is based on a novel ringing removal deconvolution algorithm, which adds a ringing detection term as regularization in the iterative process of the Richardson–Lucy algorithm. Taking into account the structural difference between the texture and the flat area, the image is segmented into several blocks and restored through adaptive iterative texture maps based on the pixel intensity and texture features of the image. In order to obtain a reasonable texture map, a Gaussian mixture model is used to fit the pixel intensity distribution, and use the expectation maximization algorithm and local binary mode to estimate. Experimental results and quantitative evaluations show that TRL can effectively reduce ringing artifacts while retaining details and achieving robustness to suppress ringing of different blur kernels. The processing time of a single 1 million pixel image in an 8-core CPU environment is about 3.5 s. And the PSNR and SSIM parameters are above 30 dB and 0.92, respectively. In conclusion, TRL is superior to the current popular algorithms.

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.

Motion Deblurring Analysis for Underwater Image Restoration

Exploration of Underwater Images will be a challenging task due to its degradations by haze, blur, colour cast and noise. In order to extract the latent information from the blurred portion, its parameters are estimated and various algorithms were analysed. Initially, length and angle of motion blurred image are estimated. Then, filters and restoration algorithms were implemented for controlling of ringing artifacts, removal of noise and latent information restoration. Some of the model based classical methods were implemented and analysed for underwater motion blurred image restoration.

Motion Blur Image Restoration by Multi-Scale Residual Neural Network

Abstract Blind deblurring is a basic subject of computer vision and image processing. Motion image deblurring is divided into non blind deblurring and blind deblurring by whether to estimate the blur kernel. Blind deblurring is easy to produce motion artifacts because of the inaccurate estimation of the blur kernel. Non blind deblurring is the best choice for the current blurred image processing. The purpose of this paper is to further improve the definition of blurred image, restore the edge information of contour, and strengthen the repair of texture details. Based on the multi-scale convolution neural network, a multi-scale residual network is proposed, which can comprehensively extract image features, enhance image feature fusion, and constrain image generation by combining multi-scale loss function with anti loss function. The performance of the algorithm is evaluated by testing the peak signal to noise ratio (PSNR) structure similarity and restoration time of the generated image relative to the clear image. This algorithm improves the average PSNR on GOPRO testset, and reduces the recovery time accordingly. It can successfully recover the detail information lost due to motion blur. This algorithm has simple network structure, strong robustness and good restoration effect, and is suitable for dealing with various image degradation problems caused by motion blur.

Motion-blurred Image Restoration Based on Joint Invertibility of PSFs

To implement restoration in a single motion blurred image, the PSF (Point Spread Function) is difficult to estimate and the image deconvolution is ill-posed as a result that a good recovery effect cannot be obtained. Considering that several different PSFs can get joint invertibility to make restoration well-posed, we proposed a motion-blurred image restoration method based on joint invertibility of PSFs by means of computational photography. Firstly, we designed a set of observation device which composed by multiple cameras with the same parameters to shoot the moving target in the same field of view continuously to obtain the target images with the same background. The target images have the same brightness, but different exposure time and different motion blur length. It is easy to estimate the blur PSFs of the target images make use of the sequence frames obtained by one camera. According to the motion blur superposition feature of the target and its background, the complete blurred target images can be extracted from the observed images respectively. Finally, for the same target images with different PSFs, the iterative restoration is solved by joint solution of multiple images in spatial domain. The experiments showed that the moving target observation device designed by this method had lower requirements on hardware conditions, and the observed images are more convenient to use joint-PSF solution for image restoration, and the restoration results maintained details well and had lower signal noise ratio (SNR).

PAMSGAN: Pyramid Attention Mechanism-Oriented Symmetry Generative Adversarial Network for Motion Image Deblurring

Motion blur is a common problem in optical imaging, which is caused by the relative displacement between the subject and the camera in the exposure process of the camera. This can result in motion blur of the acquired image, reduce the image resolution and affect the imaging quality. Motion blur image restoration technology uses the existing motion blur image to restore the clear image through the modeling of imaging physical process and mathematical solution without re-photographing the target scene. It has an important application value in the civil and military fields. Solving the problem of motion blur caused by camera jitter and object motion during camera imaging is a very challenging problem. When the popular generative adversarial network model is directly applied to the image blur blind removal task, serious pattern collapse phenomenon will occur. In this paper, we propose a novel motion image deblurring model based on pyramid attention mechanism-oriented symmetry generative adversarial network. This new method does not need to predict the fuzzy kernel of the blurred images, and can directly realize the blind removal of image motion blur. Based on the original CycleGan, the network structure and loss function of the symmetry generative adversarial network are improved. The accuracy of blind removal of motion images is improved, and the stability of the network is greatly enhanced in the case of limited samples. The generative network adopts the encoding and decoding structure, and introduces the feature pyramid attention mechanism. The combination of multi-scale pyramid features and attention mechanism can capture more rich advanced features to improve the model performance. In the experiment, the RMSProp algorithm is used to optimize the network training. Finally, a clear image is obtained through network adversarial training between generative and discriminant network. Experimental results on the related image blur benchmark datasets show that the restoration quality of the proposed method is higher in terms of subjective and objective evaluation. Meanwhile, the restoration results can achieve better results in subsequent object detection tasks.

基于闪动快门的互补序列对的运动模糊图像复原

基于闪动快门的互补序列对的运动模糊图像复原

Radon transform-based motion blurred silkworm pupa image restoration

Radon transform-based motion blurred silkworm pupa image restoration

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.

Single image motion deblurring with reduced ringing effects using variational Bayesian estimation

Image degradation commonly occurs when camera translation or irregular movement causes motion blur. In this paper, we remove the effects of unknown camera shake from an image using variational Bayesian estimation and the statistical characteristics of natural image gradients. We then propose a method to reduce ringing effects for image restoration that uses cyclic boundaries. Finally, we use the Richardson-Lucy algorithm to restore an image. Experimental results demonstrate that the blind restoration algorithm that is proposed in this paper can effectively remove image blurring caused by camera shake. Our algorithm can effectively suppress ringing artifacts and eliminate the effects of noise while protecting both edge and detail information. In general, the proposed motion deblurring method is effective in reducing both noise and ringing effects to enhance the quality of motion-blurred image restoration, and we obtained restored images that were clearer and more reliable.

Motion Blurred Image Restoration Based on Improved Genetic Algorithm

Motion Blurred Image Restoration Based on Improved Genetic Algorithm

Research on Cross-Correlative Blur Length Estimation Algorithm in Motion Blur Image

This paper proposes a motion blur length estimation method that is applied to motion blur image restoration. This method applies a cross-correlation algorithm to multi-frame motion-degraded images. In order to find the motion blur parameters, the Radon transform method is used to estimate the motion blur angle. We extract the gray value of pixels around the blur center, calculate the correlation for obtaining motion blur length, and use the Lucy-Richardson iterative algorithm to restore the degraded image. Experiment results show that this method can accurately estimate blur parameters, reduce noise, and obtain better restoration results. The method achieves good results on artificially blurred images and natural images (by the camera shake). The advantage of our algorithm that uses the Lucy-Richardson restoration algorithm compared with the Wiener filtering algorithm is made obvious with less computation time and better restored effects.

An effective motion-blurred image restoration approach for automated optical inspection

This paper presents a real-time restoration method for linear local motion-blurred images in automated optical inspection (AOI). The proposed approach is to firstly divide such an image into many sub-images and then detect the blurred sub-image by the gradient distribution and the cepstrum maximum. For a blurred sub-image, the blur direction and blur length are estimated in order to calculate the parameters of the point spread function (PSF). The Richardson–Lucy deconvolution algorithm and Wiener filtering are employed to restore this blurred sub-image. Through experimentation, the proposed algorithm produces good results on blurred images caused by the linear motion AOI equipment. To test its performance, the proposed algorithm is compared with other approaches by using a real captured printed circuit board (PCB) image, and it is proven to be superior to the others in terms of accuracy.

Restoration of motion-blurred image based on border deformation detection: a traffic sign restoration model.

Due to the rapid development of motor vehicle Driver Assistance Systems (DAS), the safety problems associated with automatic driving have become a hot issue in Intelligent Transportation. The traffic sign is one of the most important tools used to reinforce traffic rules. However, traffic sign image degradation based on computer vision is unavoidable during the vehicle movement process. In order to quickly and accurately recognize traffic signs in motion-blurred images in DAS, a new image restoration algorithm based on border deformation detection in the spatial domain is proposed in this paper. The border of a traffic sign is extracted using color information, and then the width of the border is measured in all directions. According to the width measured and the corresponding direction, both the motion direction and scale of the image can be confirmed, and this information can be used to restore the motion-blurred image. Finally, a gray mean grads (GMG) ratio is presented to evaluate the image restoration quality. Compared to the traditional restoration approach which is based on the blind deconvolution method and Lucy-Richardson method, our method can greatly restore motion blurred images and improve the correct recognition rate. Our experiments show that the proposed method is able to restore traffic sign information accurately and efficiently.