Research On Edge-preserving Image Denoising Based On Nonlocal Information And Its Application

The image processing technology is one of the critical technologies in the electronic packaging equipments such as Radio Frequency Identification(RFID) equipments. In the practical applications, digital images could be contaminated by noise during image acquisition and transmission due to the environment, mechanical vibration and some other factors, and severely impede vision location and the precision operation(Pick-and-Place) of the chips. Then, the performance of the RFID equipments is affected. Traditional image denoising methods did not well preserve the details when removing the noise, to this end, this paper systematically studies the denoising algorithms about impulse noise, Gaussian noise, and mixed noise, and these developed algorithms are applied to the independent development RFID equipment and the vision software platform FAMT_MV. The major research works and contributions of the thesis are as follows:(1)A novel algorithm for removing impulse noise, i.e. Eight Directional Searching-Adaptive Weighted Mean(EDS-AWM), is proposed, and it solves the drawbacks of the median-based algorithms. This thesis deeply analyzes the drawbacks of the median-based algorithms:1)in the filtering stage, the median-based algorithms did not consider the uniformity of the distribution of the pixels which used for calculating the median; 2)the filtering result was absolutely from the median pixel, and only considered the size relations of the local pixels, did not take into account other relativity and nonlocal information. On the basis, the thesis proposes a new searching method based on eight directions, that is, searching uncorrupted pixels in the eight different directions around the processed pixel, and introduces the nonlocal spirit; the spirit of the weighted mean filtering, which is used for Gaussian noise, is applied to remove impulse noise, and the weights can be adaptively calculated by using some weight function based on space similarity and gray similarity. For filtering with more pixels and other relative information, the EDS-AWM algorithm preserves more details.(2)A novel algorithm for removing Gaussian noise, i.e. Fast Nonlocal-means(FNLM), is proposed, and it solves the efficiency of the Nonlocal-means(NLM). The efficiency of the original Nonlocal-means(NLM) was very low, and it is difficult for using NLM in practical applications. In order to solve this problem, this thesis proposes a way to speed up NLM based on weight symmetry and moving average. Considering the quality may be reduced when speeding up, the presented method combines the modified weight function, and the ability of the algorithm to deal with the outlying image patches is improved. Furthermore, the thesis studies the properties of the residual image from the statistics perspective, and proves that the residual image contains some structural information in theory. And then propose a feasible framework about using the residual image for improving the denoised quality. For selecting the similar image patches, the thesis proposes a new method based on SSIM (Structural Similarity), and the stability and the robustness to the noise of the method are improved.(3)A novel algorithm for removing mixed noise based on Robust Outlyingness Ratio-Nonlocal-means(ROR-NLM) is proposed, and it solves the impulse noise detection and the problem of using NLM for removing impulse noise. The thesis proposes a new statistics called ROR for measuring the outlyingness of each pixel in the image at the first time, and the pixels are divided into four different outlyingness levels based on the ROR. Then the noise is detected in each level with different rules. In order to improve the detection precision and the robustness to the noise density, the coarse to fine strategy and the iterative strategy are used. Meanwhile, the NLM is successfully extended to remove the impulse noise by introducing a reference image for the first time, and a new framework for removing mixed noise is proposed by combining the new detection method.(4)According to the needs of the image processing technology in the RFID equipment, the State Key Laboratory of Digital Manufacturing Equipment and Technology developed the universal image processing software platform FAMT_MV. The above researches are integrated in the FAMT_MV, and the algorithms are tested by using the practical images from the RFID equipment. The FAMT_MV provides support for achieving different image processing operations in the uniform platform.