图像处理任务中广泛应用中值滤波器来平滑图像和消除幅度极大或极小的噪声,并且可以保持图像的边缘特性,不会使图像产生明显模糊。提出一种改进的空间域量子彩色图像中值滤波算法,并且在IBM Q(international business machines quantum...图像处理任务中广泛应用中值滤波器来平滑图像和消除幅度极大或极小的噪声,并且可以保持图像的边缘特性,不会使图像产生明显模糊。提出一种改进的空间域量子彩色图像中值滤波算法,并且在IBM Q(international business machines quantum)平台上进行仿真。该算法将量子彩色图像进行存储,利用量子比较器模块、量子控制交换器模块、中值计算模块和阈值模块设计量子彩色图像中值滤波的量子线路。相较于已有的量子彩色图像中值滤波算法,在进行中值滤波之前加入了阈值进行噪声判断,该算法滤波效果更好,同时时间复杂度也更低。展开更多
在地震图像采集过程中,由于存在各种影响因素,采集到的地震资料常常夹杂着各种噪声,严重影响了后续研究的准确性。因此,图像去噪成为至关重要的一步。自适应中值滤波作为一种经典的去噪方法,在去除椒盐噪声方面表现优异。近来,一种结合...在地震图像采集过程中,由于存在各种影响因素,采集到的地震资料常常夹杂着各种噪声,严重影响了后续研究的准确性。因此,图像去噪成为至关重要的一步。自适应中值滤波作为一种经典的去噪方法,在去除椒盐噪声方面表现优异。近来,一种结合传统全变差与自监督学习网络模型的方法——S2S-WTV被提出,该方法不仅拥有神经网络的强大表示能力,还有传统模型的泛化能力,在针对高斯噪声去除方面展现出了出色的性能。基于此,本文提出了一种结合传统方法与神经网络的多种噪声去噪模型,显著提升了去噪后的视觉效果与图像结构相似性。During seismic image acquisition, various factors often result in the data being noisy, severely impacting the accuracy of subsequent studies. Therefore, image denoising is critical. Adaptive median filtering, a classic denoising technique, excels at removing salt and pepper noise. Recently, S2S-WTV, a method that integrates traditional total variation with self-supervised learning network models, has been introduced. This approach combines the strong representational capabilities of neural networks with the generalization abilities of traditional models, showing outstanding performance in reducing Gaussian noise. Building on this, the paper introduces a multi-noise denoising model that combines traditional methods with neural networks, significantly enhancing visual quality and image structure similarity post-denoising.展开更多
文摘图像处理任务中广泛应用中值滤波器来平滑图像和消除幅度极大或极小的噪声,并且可以保持图像的边缘特性,不会使图像产生明显模糊。提出一种改进的空间域量子彩色图像中值滤波算法,并且在IBM Q(international business machines quantum)平台上进行仿真。该算法将量子彩色图像进行存储,利用量子比较器模块、量子控制交换器模块、中值计算模块和阈值模块设计量子彩色图像中值滤波的量子线路。相较于已有的量子彩色图像中值滤波算法,在进行中值滤波之前加入了阈值进行噪声判断,该算法滤波效果更好,同时时间复杂度也更低。
文摘在地震图像采集过程中,由于存在各种影响因素,采集到的地震资料常常夹杂着各种噪声,严重影响了后续研究的准确性。因此,图像去噪成为至关重要的一步。自适应中值滤波作为一种经典的去噪方法,在去除椒盐噪声方面表现优异。近来,一种结合传统全变差与自监督学习网络模型的方法——S2S-WTV被提出,该方法不仅拥有神经网络的强大表示能力,还有传统模型的泛化能力,在针对高斯噪声去除方面展现出了出色的性能。基于此,本文提出了一种结合传统方法与神经网络的多种噪声去噪模型,显著提升了去噪后的视觉效果与图像结构相似性。During seismic image acquisition, various factors often result in the data being noisy, severely impacting the accuracy of subsequent studies. Therefore, image denoising is critical. Adaptive median filtering, a classic denoising technique, excels at removing salt and pepper noise. Recently, S2S-WTV, a method that integrates traditional total variation with self-supervised learning network models, has been introduced. This approach combines the strong representational capabilities of neural networks with the generalization abilities of traditional models, showing outstanding performance in reducing Gaussian noise. Building on this, the paper introduces a multi-noise denoising model that combines traditional methods with neural networks, significantly enhancing visual quality and image structure similarity post-denoising.
