文献Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering提出了一种新的滤波方法BM3D,它参考了NLM在很大的领域内搜索相似块,并且在变换域进行滤波,同时考虑了空间的位置关系和块之间的特征相似性,并且采用了迭代的方法。使得下一次得到的维纳滤波器参数估计更加准确,达到了经典滤波算法的state of art。
Flexible Image Denoising with Multi-layer Conditional Feature Modulation 文章类型:关于图像去噪的文章 解决的问题:针对大范围的噪声程度下,图像的去噪问题 问题引入脉络: 图像去噪:CNN方法(DnCNN、FFDNet等)可以与传统方法BM3D相媲美 --->现实图像去噪的柔性去噪问题, --->现有方法无柔性处理不同噪声情况的能力,如...
这种方法通常称为双域图像去噪(Dual-Domain Image denoising, DDID),于2013年给出[172]。进一步将其扩展为双主滤波(Dual Do- main Filtering, DDF),在两个不同的域采用小波收缩和双边滤波作为鲁棒噪声估计。对DDID进行了扩展,引入了基于二次引导图像的引导滤波。该方法以较低的计算复杂度,优于BM3D、NLM和PLOW...
计算机视觉算法中的图像去噪(Image Denoising) 【摘要】 引言在计算机视觉领域,图像去噪是一项重要的任务,其目标是从受损的图像中恢复出清晰的图像。图像噪声是由于图像采集过程中的传感器噪声、信号传输过程中的干扰以及图像处理过程中的误差等因素引起的。去除图像中的噪声不仅可以提高图像的质量,还可以改善后续计算机视觉...
文献Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering提出了一种新的滤波方法BM3D,它参考了NLM在很大的领域内搜索相似块,并且在变换域进行滤波,同时考虑了空间的位置关系和块之间的特征相似性,并且采用了迭代的方法。使得下一次得到的维纳滤波器参数估计更加准确,达到了经典滤波算法的state of art...
Designing and Training of A Dual CNN for Image Denoising 论文地址:https://arxiv.org/pdf/2007.03951.pdf 关键词:图像去噪、双 CNN、复杂噪声 解决的问题:针对复杂的去噪任务(现实噪声图像) 基于先验的去噪方法(WNNM)需要人为预设参数以及复杂的优化方法 ...
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it is still an active area of research in image denoising [96]. However, the wavelet transform heavily relies on the selection of wavelet bases. If the selection is inappropriate, image shown in the wavelet domain cannot be well represented, which causes poor denoising effect. Therefore, this ...
Image denoising is an important low-level computer vision task, which aims to reconstruct a noise-free and high-quality image from a noisy image. With the development of deep learning, convolutional neural network (CNN) has been gradually applied and achieved great success in image denoising, ima...
摘要: We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g.,...关键词: 3-D transform shrinkage Adaptive grouping block matching image denoising sparsity ...