Using the Radon transform and morphological image processing, an algorithm for ship's wake detection in the SAR (synthetic aperture radar) image is developed. Being manipulated in the Radon space to invert the gra...Using the Radon transform and morphological image processing, an algorithm for ship's wake detection in the SAR (synthetic aperture radar) image is developed. Being manipulated in the Radon space to invert the gray-level and binary images, the linear texture of ship wake in oceanic clutter can be well detected. It has been applied to the automatic detection of a moving ship from the SEASAT SAR image. The results show that this algorithm is well robust in a strong noisy background and is not very sensitive to the threshold parameter and the working window size.展开更多
Three-dimensional (3-D) matched filtering has been suggested as a powerful processing technique for detecting weak, moving IR point target immersed in a noisy field. Based on the theory of the 3-D matched filtering an...Three-dimensional (3-D) matched filtering has been suggested as a powerful processing technique for detecting weak, moving IR point target immersed in a noisy field. Based on the theory of the 3-D matched filtering and the optimal linear processing, the optimal point target detector is being analyzed in this paper. The performance of the detector is introduced in detail. The results provide a standard reference to evaluate the performance of any other point target detection algorithms.展开更多
While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising,existing methods mostly rely on simple noise assumptions,such as additive white Gaussian noise(AWG...While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising,existing methods mostly rely on simple noise assumptions,such as additive white Gaussian noise(AWGN),JPEG compression noise and camera sensor noise,and a general-purpose blind denoising method for real images remains unsolved.In this paper,we attempt to solve this problem from the perspective of network architecture design and training data synthesis.Specifically,for the network architecture design,we propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block,and then plug it as the main building block into the widely-used image-to-image translation UNet architecture.For the training data synthesis,we design a practical noise degradation model which takes into consideration different kinds of noise(including Gaussian,Poisson,speckle,JPEG compression,and processed camera sensor noises)and resizing,and also involves a random shuffle strategy and a double degradation strategy.Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability.We believe our work can provide useful insights into current denoising research.The source code is available at https://github.com/cszn/SCUNet.展开更多
数字图像处理是计算机视觉和计算机图形学的重要分支,已经成为当下广泛应用的研究领域。研究旨在探索数字图像处理技术及其在计算机视觉领域中的应用,将深入研究数字图像的增强、分割、识别与分类等处理方法,为实现图像自动化分析提供...数字图像处理是计算机视觉和计算机图形学的重要分支,已经成为当下广泛应用的研究领域。研究旨在探索数字图像处理技术及其在计算机视觉领域中的应用,将深入研究数字图像的增强、分割、识别与分类等处理方法,为实现图像自动化分析提供技术支撑。研究表明,在数字图像增强处理中,采用直方图均衡化、灰度化等方法可以明显提高图片质量,在分割处理中,采用聚类算法和分水岭算法可以较好地实现图像的分割,识别与分类方面,采用尺度不变特征转换(scale-invariant feature transform,SIFT)、方向性FAST特征点检测和旋转BRIEF描述子(oriented FAST and rotated BRIEF,ORB)算法等可以更好地对图片进行识别和分类。展开更多
基金This project was supported by the National Natural Science Foundation of China (No. 49831060).
文摘Using the Radon transform and morphological image processing, an algorithm for ship's wake detection in the SAR (synthetic aperture radar) image is developed. Being manipulated in the Radon space to invert the gray-level and binary images, the linear texture of ship wake in oceanic clutter can be well detected. It has been applied to the automatic detection of a moving ship from the SEASAT SAR image. The results show that this algorithm is well robust in a strong noisy background and is not very sensitive to the threshold parameter and the working window size.
文摘Three-dimensional (3-D) matched filtering has been suggested as a powerful processing technique for detecting weak, moving IR point target immersed in a noisy field. Based on the theory of the 3-D matched filtering and the optimal linear processing, the optimal point target detector is being analyzed in this paper. The performance of the detector is introduced in detail. The results provide a standard reference to evaluate the performance of any other point target detection algorithms.
基金This work was partly supported by the ETH Zürich Fund(OK),and by Huawei grants.
文摘While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising,existing methods mostly rely on simple noise assumptions,such as additive white Gaussian noise(AWGN),JPEG compression noise and camera sensor noise,and a general-purpose blind denoising method for real images remains unsolved.In this paper,we attempt to solve this problem from the perspective of network architecture design and training data synthesis.Specifically,for the network architecture design,we propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block,and then plug it as the main building block into the widely-used image-to-image translation UNet architecture.For the training data synthesis,we design a practical noise degradation model which takes into consideration different kinds of noise(including Gaussian,Poisson,speckle,JPEG compression,and processed camera sensor noises)and resizing,and also involves a random shuffle strategy and a double degradation strategy.Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability.We believe our work can provide useful insights into current denoising research.The source code is available at https://github.com/cszn/SCUNet.
文摘数字图像处理是计算机视觉和计算机图形学的重要分支,已经成为当下广泛应用的研究领域。研究旨在探索数字图像处理技术及其在计算机视觉领域中的应用,将深入研究数字图像的增强、分割、识别与分类等处理方法,为实现图像自动化分析提供技术支撑。研究表明,在数字图像增强处理中,采用直方图均衡化、灰度化等方法可以明显提高图片质量,在分割处理中,采用聚类算法和分水岭算法可以较好地实现图像的分割,识别与分类方面,采用尺度不变特征转换(scale-invariant feature transform,SIFT)、方向性FAST特征点检测和旋转BRIEF描述子(oriented FAST and rotated BRIEF,ORB)算法等可以更好地对图片进行识别和分类。
