In this paper, we present a universal deblurring method for real images without prior knowledge of the blur source. The proposed method uses the transition region of the blurred image to estimate the point spread function (PSF). It determines the main edges of the blurred image with high edge measures based on the difference of Gaussians (DoG) operator. Those edge measures are used to predict the transition region of the sharp image. By using the transition region, we select the pixels of the blurred image to form a series of equations for calculating the PSF. In order to overcome noise disturbance, the optimal method based on the anisotropic adaptive regularization is used to estimate the PSF, in which the constraints of non-negative and spatial correlations are incorporated. Once the PSF is estimated, the blurred image is effectively recovered by employing nonblind restoration. Experimental results show that the proposed method performs effectively for real images with different blur sources.
The characters at the billet ends are connected and broken due to the complex scene. Good segmentation of these
connected and broken characters is very important in the characters recognition. However, it's difficult for the existing
segmentation algorithm to deal with these connected and broken characters. A segmentation algorithm based on multiple
feature decision is proposed to divide the connected and broken billet characters. It operates by projection to divide the
characters roughly and then multiple feature decision to judge and divide the connected and broken characters. A series
of segmentation experimental results show that the algorithm proposed is more effective than traditional segmentation
algorithm in deal with the connected and broken characters.
Steel code location is the key point to realize billet detection and recognition in production line scene with complex
illumination. However, due to high temperature and complex scene in the rolling line, the steel code location at the end
of billet is quite different from optical character location with simple background and vehicle license plate location. In
the process of billet detection and recognition, how to determine steel code target location at the end of billet from the
complex illumination scene is first necessary in steel intelligent recognition system. In order to solve this problem, a
novel method for steel code location is proposed in this paper. First of all, production line scene image is restrained by
Mean Shift filtering and iterative segmentation filter, and then candidate character region can be found by clustering
character connected domain with same features. At last, the quantitative model is established for candidate region and
the statistical decision algorithm can be used to complete screening object region. The experimental results show that the
proposed location method is very precise in most different scenes.
We present a novel algorithm to remove motion blur from a single blurred image. To estimate the unknown motion blur kernel as accurately as possible, we propose an adaptive algorithm using anisotropic regularization. The proposed algorithm preserves the point spread function (PSF) path while keeping the properties of the motion PSF when solving for the blur kernel. Adaptive anisotropic regularization and refinement of the blur kernels are incorporated into an iterative process to improve the precision of the blur kernel. Maximum likelihood (ML) estimation deblurring based on edge-preserving regularization is derived to reduce artifacts while avoiding oversmoothing of the details. By using the estimated blur kernel and the proposed ML estimation deblurring, the motion blur can be removed effectively. The experimental results for real motion blurred images show that the proposed algorithm can removes motion blur effectively for a variety of real scenes.
A double recursive algorithm based on fuzzy entropy for image thresholding is proposed. The inner recursive step is to
calculate the threshold between the given gray level interval, and the outer recursive step is to calculate the optimal for
the image with small objects. Experiments on steel billet image show that the proposed algorithm can threshold image
fast and exactly, which has effectiveness and application for real time image segmentation.
A method for real-time fast detection and symptom analysis of cracks in highway asphalt pavement is proposed. At the
first step, the fissure characteristic of the acquired and de-noised pavement images is analyzed and then extracted by the
method based on gray value comparison between the current pixel and its neighboring pixels, the false cracks are deleted
by using the computed measurement of crack features, thus the true fissures are detected. The most important step is the
symptom analysis of the cracks in the pavement image, all the data could be analyzed and be the basis for the agencies to
remedy and manage the pavement. Quantities of images are processed and the results show that the proposed method can
detect the pavement distress information actually, and has robustness and availability.
For the requirement of fast restoration, an algorithm based on multi-scale blind deconvolution is proposed by ing the multi-scale technology based on wavelet transforms. In the condtion of without knowing the point spread function (PSF), two observed images will be used as the inputs, and the two PSFs at large scale can be estimated by using of the data of the two low-frequency subband images and by incorporating some constraint regularizations. Deblurring is made in the low-frequency subband of the two images while restraining noises and preserving details in other three high-frequency subbands. A series of experiments have been performed to verify the effectiveness of the proposed algorithm.
COG (chip on glass) bonding is widely used in LCD industry. The alignment of marks in COG bonding needs high
precision and reliability. It is of utmost importance to find two marks in COG fast and exactly. Its key technique is to use
the advanced optical system to get the two-marked positions of chip on the glass and to use the PLC procedure control
to complete the automatic alignment A series of experiments are performed to test the algorithms proposed, which show
that the fast alignment of marks in COG bonding based on multi-resolution is both rational and highly effective.
A nonlinear regularization method is presented for the restoration of aero-optical degraded images, in which two frames
of short-exposure images are used to construct a series of equations to estimate the discrete values of the stochastic
turbulent point spread functions (PSF). In order to overcome the interference of noise, an optimization algorithm to
estimate the PSF values based on nonlinear regularizations in which a priori knowledge of the PSF values being
non-negative and spatial smoothing is incorporated into the process of estimation is proposed. A series of experiments
have been performed to test the proposed algorithm, which show that the proposed nonlinear regularization optimization
method is advanced and effective.
For the problem of restoration of turbulence-degraded images, it is of utmost importance to make a correct estimation of the turbulence' s stochastic point spread function (PSF). A new method is presented for estimating the discrete values of overall PSFs of turbulence-degraded images. For this method, two short-exposure turbulence-degraded images are used as the inputs, for which the Fourier transforms are made and a series of equations for calculating the discrete values of the turbulence PSFs are developed. Some effective rules for selecting equations have been worked out to ensure a reliable solution for the PSFs. To overcome the interference of noise, two optimization algorithms for estimating the turbulence PSF values, based on quadratic and nonquadratic regularization that can be incorporated into the estimation process, are proposed, in which the constraints of the PSF values are non-negative and smooth [quadratic regularization non-negative and smooth (QRNNS) and nonquadratic regularization non-negative and smooth (NQRNNS)]. A series of experiments are performed to test the algorithms proposed, which show that the NQRNNS algorithm is both rational and highly effective.
We present a novel approach to restoring images blurred by rotational motions, without experiencing geometric coordinate transformations as in traditional restoration. The space-variant blur is decomposed into a series of space-invariant blurs along the blurring paths. By incorporating Bresenham's algorithm into our work, the blurred gray values of the discrete pixels can be fetched along the blurring paths in real time. Thus, the space-variant blur can be quickly removed along the blurring paths. We apply a two-stage process to restore the rectangular blurred image, which results in the proposal of two corresponding restoration algorithms. One removes the blur by deconvolution along the blurring paths, which are completely inside the rectangular image. The other is used in the case when only some of the pixels of some blurring paths are inside the rectangular image, so based on a neighborhood knowledge guide, the information of these pixels is restored with the least cost in terms of the constrained optimization estimation theory. Furthermore, these two restoration algorithms avoid iteration calculations and some time-consuming operations. To determine the blur center and the blur extents from the blurred image in a case of not knowing the rotational motion parameters, we present, based on cross correlation, an effective blur identification method, which becomes an integral part of the proposed approach. The experimental results demonstrate the efficiency of the proposed restoration algorithms and the effectiveness of the blur identification method.