Motion Blur Direction Research Articles

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.

Identification of Motion Blur Direction Using Bresenham Algorithm for Straight Line

Identification of Motion Blur Direction Using Bresenham Algorithm for Straight Line

Parameter Estimation and Restoration of Motion Blurred Image

according to the real motion blur image restoration problems, analyze the difference between the image features and Simulation of real blurred images, this paper proposes a method that applied to real image degradation parameter estimation. First calculate the degraded image using cepstrum, taking the cepstrum to binary image using absolute value of minimum gray as the threshold, and then remove the center cross bright line; and then use formula of point to line to calculate the distance of bright fringe direction of binary image, that is direction of motion blur; the direction of motion blur is rotated to the horizontal direction by the degraded image center of rotation axis, divided the autocorrelation method to calculate fuzzy scale. To estimate the point spread function is take into the Wiener filtering algorithm to recover images, image restoration effect prove that parameter estimation results are correct.

Fractional directional derivative and identification of blur parameters of motion-blurred image

The blur direction and extent of motion-blurred image, which is degraded by the relative motion between the camera and object scene, are needed in the methods of image restoration, such as blind deconvolution. The recently developed identification method is based on integer-order derivative, which can directly extract the blur angle and extent from blurred image itself. However, this method is sensitive to noise. As an extension to the fractional-order derivative, a noncausal fractional-order directional derivative operator is derived. Based on this new operator, a novel method identifying blur parameters is developed in this work. The performance comparison between the fractional-order and integer-order methods are also presented, which demonstrate that the former provides better immunity to noise and capacity to identify the motion-blur direction and extent.

PSF Estimation and Image Restoration for Motion Blurred Images

Point spread function (PSF) estimation and image restoration algorithm are the hotspots In the research of motion blurred image restoration. In order to improve the efficacy of image restoration, an improved algorithm named quadric transforms (QT) method is proposed in this paper by analyzing the restoration process of motion blurred images. Firstly, Fourier transform and homomorphism transform are applied to the original motion blurred image, and then the Fourier transform and homomorphism transform are used again to the obtained spectrum image. Secondly, the motion blur direction is estimated by Radon transform. Thirdly, the motion blur length is found by differential autocorrelation operations. Finally, utilizing the estimated blur direction and blur length, the motion blurred image is restored by Wiener filtering. Experimental results show that the proposed QT method can get more accurate estimated motion blur angles than the primary transform (PT, that is, Fourier transform and homomorphism transform are used one time) method and can get better restored images under the meaning of peak signal to noise ratio (PSNR).

运动与离焦模糊图像的复原

Due to the image blur caused by defocus and motion,a new method is presented to distinguish defocus blur and motion blur images robustly and accurately.Based on Hough transform and compared the number of highlights in the Hough matrix,this method has high accuracy,up to 100%.An improved approach for motion blur direction from motion blur image via two directional derivations is proposed which improves the precision estimation.And,an improved method is presented to calculate the edge spread function using improved Prewiit operator and Fermi function,then obtain the MTF and then restore image using Wiener filter.Some experiments are performed to validate the performance of the proposed method.Experimental results show that this method has effectiveness and strong resistance to the noise,and can work on noisy images with low SNR;when SNR is 20 dB,it can work robustly.And compared with the traditional image restoration method and model,the image restoration results are improved significantly using the proposed method.

Restoration of space-variant blurred image based on motion-blurred target segmentation

In imaging on moving target, it is easy to get spacevariant blurred image. In order to recover the image and gain recognizable target, an approach to recover the space-variant blurred image is presented based on image segmentation. Because of motion blur's convolution process, the pixels of observed image's target and background will be displaced and piled up to produce two superposition regions. As a result, the neighboring pixels in the superposition regions will have similar grey level change. According to the pixel's motion-blur character, the target's blurred edge of superposition region could be detected. Canny operator can be recurred to detect the target edge which parallels the motion blur direction. Then in the segmentation process, the whole target image which has the character of integral convolution between motion blur and real target image can be obtained. At last, the target image is restored by deconvolution algorithms with adding zeros. The restoration result indicates that the approach can effectively solve the kind of problem of space-variant motion blurred image restoration.

Linear Motion Blur Parameter Estimation in Noisy Images Using Fuzzy Sets and Power Spectrum

Motion blur is one of the most common causes of image degradation. Restoration of such images is highly dependent on accurate estimation of motion blur parameters. To estimate these parameters, many algorithms have been proposed. These algorithms are different in their performance, time complexity, precision, and robustness in noisy environments. In this paper, we present a novel algorithm to estimate direction and length of motion blur, using Radon transform and fuzzy set concepts. The most important advantage of this algorithm is its robustness and precision in noisy images. This method was tested on a wide range of different types of standard images that were degraded with different directions (between and ) and motion lengths (between and pixels). The results showed that the method works highly satisfactory for SNR dB and supports lower SNR compared with other algorithms.