Image noise filters usually assume noise as white Gaussian. However, in a capturing pipeline, noise often becomes spatially correlated due to in-camera processing that aims to suppress the noise and increase the compression rate. Mostly, only high-frequency noise components are suppressed since the image signal is more likely to appear in the low-frequency components of the captured image. As a result, noise emerges as coarse grain which makes white (all-pass) noise filters ineffective, especially when the resolution of the target display is lower than the captured image. Denoising of image approximation in coarse scale has the advantage of removing low-frequency noise, however, lack of spatial resolution degrades the image quality. This paper presents an approach for a coarse-grain removal. Our approach utilizes existing white Gaussian noise filters to address low-frequency component of spatially correlated noises, employing pixel decoupling, local shrinkage, and soft thresholding. Subjective and objective results show that the proposed approach better handles low-frequency noise compared to related work.

We propose a method for estimating the image and video noises of different types: white Gaussian (signal-independent), mixed Poissonian-Gaussian (signal-dependent), or processed (non-white). Our method also estimates the noise level function (NLF) of these types. We do so by classifying image patches based on their intensity and variance in order to find homogeneous regions that best represent the noise. We assume that the noise variance is a piecewise linear function of intensity in each intensity class. To find noise representative regions, noisy (signal-free) patches are first nominated in each intensity class. Next, clusters of connected patches are weighted, where the weights are calculated based on the degree of similarity to the noise model. The highest ranked cluster defines the peak noise variance, and other selected clusters are used to approximate the NLF. The more information we incorporate, such as temporal data and camera settings, the more reliable the estimation becomes. To account for the processed noise, (i.e., remaining after in-camera processing), we consider the ratio of low-to-high-frequency energies. We address noise variations along video signals using a temporal stabilization of the estimated noise. Objective and subjective simulations demonstrate that the proposed method outperforms other noise estimation techniques, both in accuracy and speed.

We present a fast no-reference quality measurement method based on the entropy analysis of the image structure. The information that an images carries is represented not only by intensity values but also their position in the image. We examine the behavior of the entropy by altering the positions to distinguish the image structure, noise and blur. We propose a method to rearrange the pixel placements using a random pixel scattering transform. Using this transform we firstly determine the local power of image structure relative to noise and then we obtain the quality index by combining this information with our proposed blur factor. We show that this method is useful to select denoising parameters automatically in both unprocessed (white Gaussian) and processed (frequency-dependent) noise when the reference image is not available. Our method is easy to implement and yet rivals state-of-the-art quality measurement approaches.

In this paper, we propose a fast temporal video filter which aims to minimize blur and blocking artifacts, and handle signal-dependent noise. In order to overcome motion blur problems in block-based temporal filters, an accurate motion detection has been developed using two levels of reliability. At the first level, we use temporal data blocks to coarsely estimate local motion error and noise. Then at a finer level, averaging weights are calculated utilizing fast convolution operations. Utilizing regional noise re-estimation and the noise level function (in the case that it is known), our method is designed to adapt to signal-dependent noise and noise overestimation. Motion vectors are estimated through a fast hybrid motion estimation method which combines two conventional methods, compounding the strengths of each for a more efficient estimation. The proposed method is easy to implement and results show it rivals state-of-the-art methods in both quality and speed.

This paper presents a fast method to estimate the noise level in real images, and attempts to solve clipping and signal-dependency problems for robust noise estimation. We propose an intensity-variance homogeneity classification technique to classify images corrupted with additive Poisson-Gaussian noise based on intensity and variance. Benefiting from signal-independency in each intensity class, this method localizes the noise-representative homogenous regions in the image. Experimental results show the proposed method rivals state-of-the-art estimation approaches, while it is fast.