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FlowDenoising is a software tool that implements an adaptive Gaussian denoising filter that preserves visually appreciable structures in volumes of 3D electron microscopy (3DEM). It proceeds by nonrigidly aligning the 2D slices in each dimension, using an optical flow estimator, prior to applying a standard separable (1D) Gaussian filter. FlowDenoising has been developed in Python leveraging well-known public domain libraries, such as OpenCV and NumPy. Furthermore, the software tool exploits data-level parallelism to significantly reduce processing times. Its abilities to denoise huge volumes in just minutes on standard multicore computers makes it a useful tool in 3DEM to explore the interior of cells and tissues at the nanoscale.

The Wasserstein distance originated from the optimal transport theory is a general and flexible statistical metric in a variety of image processing problems. In this paper, we propose a novel Wasserstein driven low-dimensional manifold model (W-LDMM), which tactfully embraces the Wasserstein distance between two noise histograms and low-dimensional manifold regularization for noisy image restoration. Specifically, with the Wasserstein distance, the discrepancy between the estimated noise histogram and the reference noise histogram is minimized to improve the noise estimation accuracy. Moreover, based on the fact that the patch manifold structures of many natural images are low dimensional, the dimension of the image patch maniflod is utilized as regularization constraint. The key idea of W-LDMM is that the Wasserstein distance for noise constraint and the low-dimensional manifold for image regularization are complementary to each other, rather than isolated and uncorrelated. Together, they promote the W-LDMM to exhibit excellent restored results in Gaussian denoising and noisy image inpainting. Finally, extensive experiments confirm that our W-LDMM can greatly improve visual and quantitative performance compared to several popular image restoration models. [ABSTRACT FROM AUTHOR]

Image denoising is a fundamental operation in image processing and holds considerable practical importance for various real-world applications. Arguably several thousands of papers are dedicated to image denoising. In the past decade, state-of-the-art denoising algorithms have been clearly dominated by nonlocal patch-based methods, which explicitly exploit patch self-similarity within the targeted image. However, in the past two years, discriminatively trained local approaches have started to outperform previous nonlocal models and have been attracting increasing attention due to the additional advantage of computational efficiency. Successful approaches include cascade of shrinkage fields (CSF) and trainable nonlinear reaction diffusion (TNRD). These two methods are built on the filter response of linear filters of small size using feed forward architectures. Due to the locality inherent in local approaches, the CSF and TNRD models become less effective when the noise level is high and consequently introduce some noise artifacts. In order to overcome this problem, in this paper we introduce a multiscale strategy. To be specific, we build on our newly developed TNRD model, adopting the multiscale pyramid image representation to devise a multiscale nonlinear diffusion process. As expected, all the parameters in the proposed multiscale diffusion model, including the filters and the influence functions across scales, are learned from training data through a loss-based approach. Numerical results on Gaussian and Poisson denoising substantiate that the exploited multiscale strategy can successfully boost the performance of the original TNRD model with a single scale. As a consequence, the resulting multiscale diffusion models can significantly suppress the typical incorrect features for those noisy images with heavy noise. It turns out that multiscale TNRD variants achieve better performance than state-of-the-art denoising methods. [ABSTRACT FROM AUTHOR]

• Retaining high frequency detail information is important for image denoising. • Removing batch normalization (BN) is beneficial for image denoising. • Image restoration tasks can be finished by a single end-to-end network. Compared with the traditional image denoising method, although the convolutional neural network (CNN) has better denoising performance, there is an important issue that has not been well resolved: the residual image obtained by learning the difference between noisy image and clean image pairs contains abundant image detail information, resulting in the serious loss of detail in the denoised image. In this paper, in order to relearn the lost image detail information, a mathematical model is deducted from a minimization problem and an end-to-end detail retaining CNN (DRCNN) is proposed. Unlike most denoising methods based on CNN, DRCNN is not only focus to image denoising, but also the integrity of high frequency image content. DRCNN needs less parameters and storage space, therefore it has better generalization ability. Moreover, DRCNN can also adapt to different image restoration tasks such as blind image denoising, single image superresolution (SISR), blind deburring and image inpainting. Extensive experiments show that DRCNN has a better effect than some classic and novel methods. [ABSTRACT FROM AUTHOR]