Unpaired image-to-image translation with improved two-dimensional feature

Noise level estimation is a long-standing problem in image processing. The challenge arises from the estimation being easily affected by texture information. We propose an innovative noise level estimation method via the kurtosis test, which is a normalized fourth-order moment. The proposed method consists of two stages: the first one is to determine the image patches with normality using the kurtosis test; the noise level is then estimated from these selected normal patches in the second stage, in which the average of the standard deviations is used as the final estimation. Experimental results show that the proposed method outperforms the state-of-the-art estimation techniques in terms of noise level estimation and guided denoising. 

Image noise level estimation via kurtosis test

Abstract. Noise level estimation is a long-standing problem in image processing. The challenge arises from the estimation being easily affected by texture information. We propose an innovative noise level estimation method via the kurtosis test, which is a normalized fourth-order moment. The proposed method consists of two stages: the first one is to determine the image patches with normality using the kurtosis test; the noise level is then estimated from these selected normal patches in the second stage, in which the average of the standard deviations is used as the final estimation. Experimental results show that the proposed method outperforms the state-of-the-art estimation techniques in terms of noise level estimation and guided denoising.

This paper introduces a stochastic plug-and-play (PnP) sampling algorithm that leverages variable splitting to efficiently sample from a posterior distribution. The algorithm based on split Gibbs sampling (SGS) draws inspiration from the half quadratic splitting method (HQS) and the alternating direction method of multipliers (ADMM). It divides the challenging task of posterior sampling into two simpler sampling problems. The first problem depends on the likelihood function, while the second is interpreted as a Bayesian denoising problem that can be readily carried out by a deep generative model. Specifically, for an illustrative purpose, the proposed method is implemented in this paper using state-of-the-art diffusion-based generative models. Akin to its deterministic PnP-based counterparts, the proposed method exhibits the great advantage of not requiring an explicit choice of the prior distribution, which is rather encoded into a pre-trained generative model. However, unlike optimization methods (e.g., PnP-ADMM and PnP-HQS) which generally provide only point estimates, the proposed approach allows conventional Bayesian estimators to be accompanied by confidence intervals at a reasonable additional computational cost. Experiments on commonly studied image processing problems illustrate the efficiency of the proposed sampling strategy. Its performance is compared to recent state-of-the-art optimization and sampling methods. 

Plug-and-Play Split Gibbs Sampler: embedding deep generative priors in Bayesian inference

When subjected to a classical fluctuating field characterized by a Gaussian process, we examine the purity and coherence protection in a three-level quantum system. This symmetry of the three-level system is examined when the local random field is investigated further in the noiseless and noisy regimes. In particular, we consider fractional Gaussian, Gaussian, Ornstein–Uhlenbeck, and power law noisy regimes. We show that the destructive nature of the Ornstein–Uhlenbeck noise toward the symmetry of the qutrit to preserve encoded purity and coherence remains large. Our findings suggest that properly adjusting the noisy parameters to specifically provided values can facilitate optimal extended purity and coherence survival. Non-vanishing terms appear in the final density matrix of the single qutrit system, indicating that it is in a strong coherence regime. Because of all of the Gaussian noises, monotonic decay with no revivals has been observed in the single qutrit system. In terms of coherence and information preservation, we find that the current qutrit system outperforms systems with multiple qubits or qutrits using purity and von Neumann entropy. A comparison of noisy and noiseless situations shows that the fluctuating nature of the local random fields is ultimately lost when influenced using the classical Gaussian noises. 

/pdf/decoherence-effects-in-a-three-level-system-under-gaussian-2cpnk2hg.pdf

Decoherence Effects in a Three-Level System under Gaussian Process

Noise level is an important parameter in many visual applications, especially in image denoising. How to accurately estimate the noise level from a noisy image is a challenging problem. However, for color image denoising, it is not that the more accurate the noise level is, the better the denoising performance is, but that the noise level higher than the true noise can achieve a better denoising result. For better denoising, we propose a statistical iterative method based on low-rank image patches. We select the low-rank patches in the image and calculate the eigenvalues of the covariance matrix of these patches. Unlike the existing methods that take the smallest eigenvalue as the estimated noise level, the proposed method analyzes the relationship between the median value and the mean value of the eigenvalue according to the statistical property and selects an appropriate number of eigenvalues to average as the estimated noise level. Extensive experiments are conducted, demonstrating that our estimated noise level reaches the highest value of all comparison methods. And the denoising results on color images of our method outperform all the state-of-the-art methods and the true noise level.

Gaussian noise level estimation for color image denoising.

Multi-weighted nuclear norm minimization for real world image denoising

Noise modeling and denoising of images collected by on-board track inspection system

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Gaussian noise level estimation for color image denoising.

Multi-weighted nuclear norm minimization for real world image denoising

Noise modeling and denoising of images collected by on-board track inspection system