In recent years, accurate Gaussian noise removal has attracted considerable attention for mobile applications, as in smart phones. Accurate conventional denoising methods have the potential ability to improve denoising performance with no additional time. Therefore, we propose a rapid post-processing method for Gaussian noise removal in this paper. Block matching and 3D filtering and weighted nuclear norm minimization are utilized to suppress noise. Although these nonlocal image denoising methods have quantitatively high performance, some fine image details are lacking due to the loss of high frequency information. To tackle this problem, an improvement to the pioneering RAISR approach (rapid and accurate image super-resolution), is applied to rapidly post-process the denoised image. It gives performance comparable to state-of-the-art super-resolution techniques at low computational cost, preserving important image structures well. Our modification is to reduce the hash classes for the patches extracted from the denoised image and the pixels from the ground truth to 18 filters by two improvements: geometric conversion and reduction of the strength classes. In addition, following RAISR, the census transform is exploited by blending the image processed by noise removal methods with the filtered one to achieve artifact-free results. Experimental results demonstrate that higher quality and more pleasant visual results can be achieved than by other methods, efficiently and with low memory requirements.

近年来，精确的高斯噪声消除已在移动应用（例如智能手机）中引起了相当大的关注。准确的常规降噪方法具有无需额外时间即可提高降噪性能的潜在能力。因此，本文提出了一种快速的高斯噪声去除后处理方法。块匹配和3D滤波以及加权核规范最小化被用来抑制噪声。尽管这些非局部图像去噪方法在定量上具有高性能，但是由于高频信息的丢失而缺少一些精细的图像细节。为了解决这个问题，对开创性的RAISR方法（快速和准确的图像超分辨率）进行了改进，以对后的去噪图像进行快速后处理。它以较低的计算成本提供了与最新超分辨率技术相当的性能，并很好地保留了重要的图像结构。我们的修改是通过以下两种改进将通过降噪处理的图像从降噪图像中提取的哈希值和从地面真实像素中提取的像素的哈希值类别减少到18个滤波器：几何转换和强度类别的减少。此外，在进行RAISR之后，通过将经过噪声去除方法处理的图像与经过滤波的图像进行混合来开发普查变换，从而获得无伪影的结果。实验结果表明，与其他方法相比，可以高效且低内存需求地实现更高质量和更令人愉悦的视觉效果。我们的修改是通过以下两种改进将通过降噪处理的图像从降噪图像中提取的哈希值和从地面真实像素中提取的像素的哈希值类别减少到18个滤波器：几何转换和强度类别的减少。此外，在进行RAISR之后，通过将经过噪声去除方法处理的图像与经过滤波的图像进行混合来开发普查变换，从而获得无伪影的结果。实验结果表明，与其他方法相比，可以高效且低内存需求地实现更高质量和更令人愉悦的视觉效果。我们的修改是通过以下两种改进将通过降噪处理的图像从降噪图像中提取的哈希值和从地面真实像素中提取的像素的哈希值类别减少到18个滤波器：几何转换和强度类别的减少。此外，在进行RAISR之后，通过将经过噪声去除方法处理的图像与经过滤波的图像进行混合来开发普查变换，从而获得无伪影的结果。实验结果表明，与其他方法相比，可以高效且低内存需求地实现更高质量和更令人愉悦的视觉效果。通过将经过噪声去除方法处理的图像与经过滤波的图像进行混合，可以开发出普查变换，从而获得无伪影的结果。实验结果表明，与其他方法相比，可以高效且低内存需求地实现更高质量和更令人愉悦的视觉效果。通过将经过噪声去除方法处理的图像与经过滤波的图像进行混合，可以开发出普查变换，从而获得无伪影的结果。实验结果表明，与其他方法相比，可以高效且低内存需求地实现更高质量和更令人愉悦的视觉效果。