Author
Su Bo
Bio: Su Bo is an academic researcher. The author has contributed to research in topics: Non-local means & Curvelet. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.
Papers
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TL;DR: The experiment result shows that the proposed new algorithm can get high peak signal to noise ratio, visual effect is very good image and the feasibility of this method is proved by the experimental results.
Abstract: Aiming at the limitations of the wavelet transform in image denoising, this paper proposes a new image denoising algorithm based on curvelet transform mathematical method. In this paper, the feasibility of this method is proved by the experimental results. The experiment result shows that, using the proposed new algorithm can get high peak signal to noise ratio, visual effect is very good image.
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Journal Article•
TL;DR: The experimental results show that the preprocess algorithm is able to obtain a satisfactory threshold for the binarization of the test image and the process speed has met the requirement of the real-time inspection.
Abstract: A preprocess algorithm consisting of denoising and binarization for PCB test image is proposed.The algorithm smooths the gray histogram of the test image with Harr wavelet to remove the "pseudo valley",and fixes a range of the threshold according to the prior information in the standard image,and then finds out the valley of the histogram in the range and that is the threshold.The experimental results show that the preprocess algorithm is able to obtain a satisfactory threshold for the binarization of the test image.After the preprocess,the objects in the binarized test image have clear boundaries and all the defects are preserved,while the noise which could influence on subsequent processes has been almost removed.In addition,the process speed has met the requirement of the real-time inspection.
1 citations
TL;DR: Zhang et al. as discussed by the authors proposed a generative adversarial network-based approach to convert a degraded neutron image into a clean image by integrating an attention mechanism into the generation and discrimination networks.
Abstract: Neutron radiography has been widely employed in nondestructive investigations. The noise and blur that are inevitably generated in the process of neutron radiography seriously decrease the image quality and lead to the loss of image information. In particular, white spot noise, which has the characteristics of nonuniform distribution, large size, and high magnitude, is difficult to remove. To solve this issue, we apply a generative adversarial network-based to convert a degraded neutron image into a clean image. The basic idea is to integrate attention mechanisms into our generation and discrimination networks. A fusion block with a visual attention mechanism is proposed, which can extract more potential features from the images and preserve the image texture as much as possible. In addition, due to the lack of neutron image datasets, we propose a neutron image degradation model to simulate the noise and blur in real neutron images. The results of the experiments show that our method can effectively eliminate noise and blur from neutron images while retaining texture information well.