To improve the quality of the pepper and salt noised image, an adaptive mean filter algorithm is proposed. Firstly, the appropriate filter window size is automatically selected according to the intensity of noise in the image. Secondly, whether each pixel is a pepper and salt noise point is detected . The noised point outputs the mean gray value of the unnoised pixels in the current window. The non-noised point outputs the original value. Thirdly, the denoising effect of the three algorithms was compared by simulation experiments, and the influence of different pepper and salt noise density on denoising effect was tested. From the objective and subjective judgement, the propose method achieves higher PSNR and better visual effect.
Point cloud registration is an important research problem in machine vision. One of the most widely used methods for point set registration is the iterative closest point (ICP) algorithm. However, ICP is known to be susceptible to local minima because it is based on the local iterative optimization technique and its performance critically relies on the quality of the initialization. To reduce the possibility of the algorithm falling into local minima, we propose an iterative algorithm based on geometric distance and local feature weighting named weighted correlation coefficient iterative closest point algorithm (WCC_ICP). The algorithm first establishes the corresponding relationship of points using linear representation and constructs an optimization model with constraints. Subsequently, the constrained optimization model is converted into an unconstrained optimization model by Lagrangian multiplication. Finally, an iterative technique is used to solve the optimal rigid body transformation of the optimized model. In addition, in each iteration, the identification ability of the local feature operator of points is used to repair the corresponding relation of points, which reduces the risk of the iterative algorithm falling into the local minima. Numerical experiments show that the proposed WCC_ICP algorithm reduces the risk of the iterative algorithm falling into local minima.
Blind image restoration has always been a hot and difficult problem in the field of image processing. As the vertical motion blur image has obvious characteristics in the frequency domain, the distance between the center point and the nearest horizontal black strip is automatically estimated by setting thresholds, and the estimated vertical motion point spread function is obtained; the image is blindly restored by the constrained least squares algorithm. Experimental results show that the proposed vertical motion point spread function estimation algorithm has high accuracy, and the restored image has better visual effects and higher PSNR.
There are different degrees of “pepper γ salt” noise in most acquired images. To overcome the shortcomings of traditional median filtering algorithm, an adaptive median filtering algorithm is proposed. Firstly, the appropriate filter window size is automatically selected according to the intensity of noise in the image. Secondly, whether each pixel is a noise point is judged. The noise point outputs the median value and the non-noise point outputs the original value. Thirdly, the denoising effect of the two algorithms was compared by simulation experiments, and the influence of different “pepper & salt” noise density on denoising effect was tested. From the perspective of subjective visual effect and objective PSNR value, the denoising effect of adaptive median filtering is better than that of traditional median filtering.
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