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Handbook Of Image And Video Processing

In one embodiment, a method includes accessing a plurality of generative adversarial networks (GANs) that are each applied to a particular level k of a Laplacian pyramid. Each GAN may comprise a generative model G k and a discriminative model D k . At each level k, the generative model G k may take as input a noise vector z k and may output a generated image {tilde over (h)} k . At each level k, the discriminative model D k may take as input either the generated image {tilde over (h)} k or a real image h k , and may output a probability that the input was the real image h k . The method may further include generating a sample image Ĩ k from the generated images {tilde over (h)} k , wherein the sample image is based on the probabilities outputted by each of the discriminative models D k and the generated images {tilde over (h)} k . The method may further include providing the sample image Ĩ k for display.

Producing higher-quality samples of natural images

We propose a non-iterative image deconvolution algorithm for data corrupted by Poisson or mixed Poisson-Gaussian noise. Many applications involve such a problem, ranging from astronomical to biological imaging. We parameterize the deconvolution process as a linear combination of elementary functions, termed as linear expansion of thresholds. This parameterization is then optimized by minimizing a robust estimate of the true mean squared error, the Poisson unbiased risk estimate. Each elementary function consists of a Wiener filtering followed by a pointwise thresholding of undecimated Haar wavelet coefficients. In contrast to existing approaches, the proposed algorithm merely amounts to solving a linear system of equations, which has a fast and exact solution. Simulation experiments over different types of convolution kernels and various noise levels indicate that the proposed method outperforms the state-of-the-art techniques, in terms of both restoration quality and computational complexity. Finally, we present some results on real confocal fluorescence microscopy images and demonstrate the potential applicability of the proposed method for improving the quality of these images.

PURE-LET Image Deconvolution

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.

Estimation of Gaussian, Poissonian–Gaussian, and Processed Visual Noise and Its Level Function

This study presents a model to effectively recognise image noise of different types and levels: impulse, Gaussian, Speckle and Poisson noise, and a mixture of multiple types of the noise. To classify image noise type, the convolutional neural network (CNN) method with backpropagation algorithm and stochastic gradient descent optimisation techniques are implemented. In order to reduce the training time and computational cost of the algorithm, the principal components analysis (PCA) filters generating strategy is deployed to obtain data adaptive filter banks. The authors validated their designed CNN with PCA for noise types recognition model with degraded images containing noise of single and combination of multiple types, with a total of 11,000 and 1650 datasets for training and testing purposes, respectively. The variety and complexity of data have never been addressed before in any other research work. The capability of their intelligent system in handling images degraded under this complicated environment has surpassed human-eye performance in noise types recognition. The authors' experiments have proven the reliability of the proposed noise types recognition model by having achieved an overall average accuracy of 99.3% while recognising eight classes of noise.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2017.0374

Image noise types recognition using convolutional neural network with principal components analysis

A time-space domain video denoising method is provided which reduces video noise of different types Noise is assumed to be real-world camera noise such as white Gaussian noise (signal-independent), mixed Poissonian-Gaussian (signal-dependent) noise, or processed (non-white) signal-dependent noise This method comprises the following processing steps: 1) time-domain filtering on current frame using motion-compensated previous and subsequent frames; 2) restoration of possibly blurred contents due to faulty motion compensation and noise estimation; 3) spatial filtering to remove residual noise left from temporal filtering To reduce the blocking effect, a method is applied to detect and remove blocking in the motion compensated frames To perform time-domain filtering weighted motion-compensated frame averaging is used To decrease the chance of blurring, two levels of reliability are used to accurately estimate the weights

Time-space methods and systems for the reduction of video noise

In image and video denoising, a quantitative measure of genuine image content, noise, and blur is required to facilitate quality assessment, when the ground truth is not available. In this paper, we present a no-reference image quality assessment for denoising applications, which examines local image structure using orientation dominancy and patch sparsity. We propose a fast method to find the dominant orientation of image patches, which is used to decompose them into singular values. Combining singular values with the sparsity of the patch in the transform domain, we measure the possible image content and noise of the patches and of the whole image. We show that the proposed method is useful to select parameters of denoising algorithms automatically in different noise scenarios such as white Gaussian and processed noise. Our objective and subjective results confirm the correspondence between the measured quality and the ground truth. We show that the proposed method rivals related state-of-the-art no-reference quality assessment approaches.

Sparsity-based no-reference image quality assessment for automatic denoising

A method is provided to estimate image and video noise 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 noises. This is done by classification of intensity variances of image patches in order to find homogeneous regions that best represent the noise. It is assumed 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.

Methods and systems for the estimation of different types of noise in image and video signals

Image denoising is a well explored but still an active research topic. The focus is usually on achieving higher numerical quality which is theoretically interesting, however, often the factor of computation cost is not considered. Our idea is to employ different image Gaussian noise filters to construct an effective image denoiser, where the deficiency of each filter is compensated with others, while a wide variation of quality versus speed can be achieved. We integrate filters using different cascaded forms and show that if two filters use uncorrelated features, their cascaded form provides a higher quality than each separately. We start with easy-to-implement filters employing pixel- and frequency-domain with different kernel size to construct a fast yet high-quality multi-domain denoiser. Then, we propose more complex denoisers by integrating our cascaded multi-domain denoiser to other state-of-the-art denoising methods. Simulations show that the quality of proposed multi-domain denoiser is significantly higher than its building-blocks. We also show that the proposed multi-domain denoiser can be integrated to state-of-the-art denoisers to from a more effective denoiser, while adding negligible complexity.

Meisam Rakhshanfar

Papers

Estimation of Gaussian, Poissonian–Gaussian, and Processed Visual Noise and Its Level Function

Time-space methods and systems for the reduction of video noise

Sparsity-based no-reference image quality assessment for automatic denoising

Methods and systems for the estimation of different types of noise in image and video signals

Efficient cascading of multi-domain image Gaussian noise filters