Aiming at the inaccuracy of Non-Local Means (NLM) algorithm for measuring the similarity of neighborhood blocks, an improved Non-Local Means denoising algorithm based on Difference Hash (dHash) algorithm and Hamming distance is proposed. The traditional algorithm measures the similarity between neighborhood blocks by Euclidean distance, so the ability to preserve edges and details is weak, which leads to the blurred and distorted images after filtering. To this end, the Difference Hash algorithm containing the gradient information is introduced, the difference hash images are generated from neighborhood blocks, and the Hamming distance of the difference hash images is calculated to measure the similarity of the neighborhood blocks. Finally, the Euclidean distance is improved. Experiment results show that the proposed method can preserve edges and details while denoising the low-noise images. Compared with other improved algorithms, the running speed of the proposed algorithm is also greatly improved, which has a certain application value.
KEYWORDS: 3D modeling, 3D image reconstruction, Feature extraction, Visual process modeling, Digital image processing, Image compression, 3D vision, Clouds, Phase measurement, Reconstruction algorithms
The free-form surface does not has stable texture and feature points. The existing 3D reconstruction algorithms for the free-form surface extract uncertain quantity of feature points which appear in random location because of image noise and different brightness. A novel 3D reconstruction method through projecting a grid on free-form surface is presented. Gridlines are treated as texture on the free-form surface. On the basis, the feature texture is extracted and feature points are precisely matched, the 3D reconstruction is completed according to recognizing and extracting the grid feature. The method is verified its feasibility and validity after applying it on several models.
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