Restoration Of Gray Scale Images Research Articles

Generation of PAS-stained images of glomerular tissue units using a generative adversarial network with spectral normalization colorization method

In recent years, the pathological diagnosis of glomerular diseases typically involves the study of glomerular his-to pathology by specialized pathologists, who analyze tissue sections stained with Periodic Acid-Schiff (PAS) to assess tissue and cellular abnormalities. In recent years, the rapid development of generative adversarial networks composed of generators and discriminators has led to further developments in image colorization tasks. In this paper, we present a generative adversarial network by Spectral Normalization colorization designed for color restoration of grayscale images depicting glomerular cell tissue elements. The network consists of two structures: the generator and the discriminator. The generator incorporates a U-shaped decoder and encoder network to extract feature information from input images, extract features from Lab color space images, and predict color distribution. The discriminator network is responsible for optimizing the generated colorized images by comparing them with real stained images. On the Human Biomolecular Atlas Program (HubMAP)—Hacking the Kidney FTU segmentation challenge dataset, we achieved a peak signal-to-noise ratio of 29.802 dB, along with high structural similarity results as other colorization methods. This colorization method offers an approach to add color to grayscale images of glomerular cell tissue units. It facilitates the observation of physiological information in pathological images by doctors and patients, enabling better pathological-assisted diagnosis of certain kidney diseases.

Generation of PAS-Stained images of glomerular tissue units using a generative adversarial network with spectral normalization colorization method

In recent years, the pathological diagnosis of glomerular diseases typically involves the study of glomerular his-to pathology by specialized pathologists, who analyze tissue sections stained with Periodic Acid-Schiff (PAS) to assess tissue and cellular abnormalities. In recent years, the rapid development of generative adversarial networks composed of generators and discriminators has led to further developments in image colorization tasks. In this paper, we present a generative adversarial network by Spectral Normalization colorization designed for color restoration of grayscale images depicting glomerular cell tissue elements. The network consists of two structures: the generator and the discriminator. The generator incorporates a U-shaped decoder and encoder network to extract feature information from input images, extract features from Lab color space images, and predict color distribution. The discriminator network is responsible for optimizing the generated colorized images by comparing them with real stained images. On the Human Biomolecular Atlas Program (HubMAP)—Hacking the Kidney FTU segmentation challenge dataset, we achieved a peak signal-to-noise ratio of 29.802 dB, along with high structural similarity results as other colorization methods. This colorization method offers an approach to add color to grayscale images of glomerular cell tissue units. It facilitates the observation of physiological information in pathological images by doctors and patients, enabling better pathological-assisted diagnosis of certain kidney diseases.

Iterative rank-one matrix completion via singular value decomposition and nuclear norm regularization

Matrix completion is widely used in many fields. In existing matrix completion methods, such as rank minimization and matrix factorization, the hyperparameters must be learned. However, hyperparameter tuning is a time-consuming and tedious process. In this paper, we propose a novel matrix completion method called IMC, i.e., iterative rank-one matrix completion via singular value decomposition (SVD) and nuclear norm regularization. First, we construct a rank-one matrix completion model using nuclear norm regularization. Then, the variables to be optimized in the model are divided into several blocks. Finally, the blocks are iteratively optimized one by one until convergence. For the optimization of each block, we propose an efficient solution scheme based on SVD, in which only the maximum singular value and leading singular vectors of a sparse matrix must be calculated. Further, a nonparametric singular value penalty function is designed to ensure a low-rank completion matrix. In addition, the optimization of each block uses only the values inside the observed entries; hence, no errors are accumulated. Test results shows that the proposed method converges rapidly and outperforms some state-of-the-art methods when applied to grayscale image restoration, recommendation systems, and vote networks.

Image Restoration Based on Stochastic Resonance in a Parallel Array of Fitzhugh–Nagumo Neuron

The poor denoising effect for noisy grayscale images with traditional processing methods would be obtained under strong noise condition, and some image details would be lost. In this paper, a parallel array model of Fitzhugh–Nagumo (FHN) neurons was proposed, which can restore noisy grayscale images well with low peak signal-to-noise ratio (PSNR) conditions and the image details are better preserved. Firstly, the row-column scanning method was used to convert the 2D grayscale image into a 1D signal, and then the 1D signal was converted into a binary pulse amplitude modulation (BPAM) signal by signal modulation. The modulated signal was input to an FHN parallel array for stochastic resonance (SR). Finally, the array output signal was restored to a 2D gray image, and the image restoration effect was analyzed based on the PSNR and Structural SIMilarity (SSIM) index. It is shown that the SR effect can be exhibited in an array of FHN neuron nonlinearities by increasing the array size, and the image restoration effect is significantly better than the traditional image restoration method, and larger PSNR and SSIM can be obtained. It provides a new idea for grayscale image restoration in a low PSNR environment.

Multi-Channel and Multi-Model-Based Autoencoding Prior for Grayscale Image Restoration.

Image restoration (IR) is a long-standing challenging problem in low-level image processing. It is of utmost importance to learn good image priors for pursuing visually pleasing results. In this paper, we develop a multi-channel and multi-model-based denoising autoencoder network as image prior for solving IR problem. Specifically, the network that trained on RGB-channel images is used to construct a prior at first, and then the learned prior is incorporated into single-channel grayscale IR tasks. To achieve the goal, we employ the auxiliary variable technique to integrate the higher-dimensional network-driven prior information into the iterative restoration procedure. In addition, according to the weighted aggregation idea, a multi-model strategy is put forward to enhance the network stability that favors to avoid getting trapped in local optima. Extensive experiments on image deblurring and deblocking tasks show that the proposed algorithm is efficient, robust, and yields state-of-the-art restoration quality on grayscale images.

Spatially Arranged Sparse Recurrent Neural Networks for Energy Efficient Associative Memory.

The development of hardware neural networks, including neuromorphic hardware, has been accelerated over the past few years. However, it is challenging to operate very large-scale neural networks with low-power hardware devices, partly due to signal transmissions through a massive number of interconnections. Our aim is to deal with the issue of communication cost from an algorithmic viewpoint and study learning algorithms for energy-efficient information processing. Here, we consider two approaches to finding spatially arranged sparse recurrent neural networks with the high cost-performance ratio for associative memory. In the first approach following classical methods, we focus on sparse modular network structures inspired by biological brain networks and examine their storage capacity under an iterative learning rule. We show that incorporating long-range intermodule connections into purely modular networks can enhance the cost-performance ratio. In the second approach, we formulate for the first time an optimization problem where the network sparsity is maximized under the constraints imposed by a pattern embedding condition. We show that there is a tradeoff between the interconnection cost and the computational performance in the optimized networks. We demonstrate that the optimized networks can achieve a better cost-performance ratio compared with those considered in the first approach. We show the effectiveness of the optimization approach mainly using binary patterns and apply it also to gray-scale image restoration. Our results suggest that the presented approaches are useful in seeking more sparse and less costly connectivity of neural networks for the enhancement of energy efficiency in hardware neural networks.

Optimum adaptive array stochastic resonance in noisy grayscale image restoration

Considering the widespread noise interference in the two-dimensional (2D) image transmission processing, we proposed an optimal adaptive bistable array stochastic resonance (SR)-based grayscale image restoration enhancement method under low peak signal-to-noise ratio (PSNR) environments. In this method, the Hilbert scanning is adopted to reduce the dimension of the original grayscale image. The 2D image signal is converted into a one-dimensional (1D) binary pulse amplitude modulation (BPAM) signal. Meanwhile, we use the adaptive bistable array SR module to enhance the 1D low SNR BPAM signal. In order to obtain the restored image, we transform the enhanced BPAM signal into a 2D grayscale image signal. Simulation results show that the proposed method significantly outperforms the classical image restoration methods (i.e., mean filter, Wiener filter and median filter) both on the grayscale level and the PSNR of the restored image, particularly in a low PSNR scenario. Larger array size brings better image restoration effect.

Multi-Noise and Multi-Channel Derived Prior Information for Grayscale Image Restoration

Image restoration is an extensively studied area with lots of outstanding algorithms developed. Nevertheless, most existing methods still have some limitations that only apply to a single tailored restoration task or suffer from long iterative reconstruction time or yield unstable results. To address these challenges, this work presents a multi-noise and multi-channel enhanced Deep Mean-Shift Prior (MEDMSP) for grayscale IR tasks. Specifically, we draw valuable high-dimensional prior knowledge by learning a multi-noise stimulated DMSP network from color images with RGB-channels. Variable augmentation technique is then adopted for incorporating the higher-dimensional network prior into the iterative reconstruction procedure. MEDMSP has been evaluated on different IR tasks and compared to a variety of state-of-the-art methods. Experimental results show that the proposed method has better capability in image deblurring and accurate compressive sensing reconstructions in terms of both visual and quantitative comparisons.

Weighted Tensor Nuclear Norm Minimization for Color Image Restoration

Non-local self-similarity (NLSS) is widely used as prior information in an image restoration method. In particular, a low-rankness-based prior has a significant effect on performance. On the other hand, a number of color extensions of NLSS-based grayscale image restoration methods have been developed. These extensions focus on the pixel-wise correlation among color channels. However, a natural color image also has a complex dependency, known as an inter-channel dependency, among local regions from different color channels. As a result, color artifacts appear in a denoised image obtained by using the existing methods. In this paper, we propose a novel non-local and inter-channel dependency-aware prior called the weighted tensor nuclear norm (WTNN). The proposed prior is derived by incorporating inter-channel dependency to low-rank-based NLSS prior. The WTNN is a low-rankness-of-the-third-order patch tensor, and we apply it to the tensors constructed with non-local similar patches. It enables us to naturally represent the higher-order dependencies among similar color patches. We propose an image denoising algorithm using the WTNN and image restoration algorithm by using a non-trivial generalization of this algorithm. The experimental results clearly show that the proposed WTNN-based color image denoising and restoration algorithms outperform state-of-the-art methods.

Restoration of blurred color images with impulse noise

Restoration of images degraded by blurring and impulse noise has received considerable attention recently. Guo et al. (2009) proposed a fast l1-total variation algorithm for grayscale image restoration with impulse noise. In this paper, we extend their idea for deblurring color images with impulse noise. An alternating iteration scheme is adopted for solving the corresponding problem. More importantly, we employ the five-point property to analyze the convergence of the proposed alternating algorithm. Numerical experiments demonstrate that the proposed method could deblur color images with good quality.

REMOVAL OF HIGH DENSITY IMPULSE NOISE USING MORPHOLOGICAL BASED ADAPTIVE UNSYMMETRICAL TRIMMED MID-POINT FILTER

A Morphological based Adaptive Unsymmetrical Trimmed Mid-Point Filter (MAUTMPF) for the restoration of gray scale images corrupted by salt and pepper n oise for varying noise densities is proposed in thi s study. Images corrupted by impulsive noise severely hinder subsequent image processing tasks, such as edge detection, image segmentation, object recognition, etc. Therefore, it is absolutely essential to resto re the original image from the corrupted image. The proposed algorithm replaces the corrupted pixel by mid po int value out of the retained pixels other than 0’s and 255’s in a 3◊3 window. The essential condition for the v alidity of the window is that at least two pixels in the selected window should be uncorrupted; if not the window size is incremented by 2. The iteration stops when the window size reaches 7. In particular case, when the con dition for validity doesn’t hold in 7◊7 window then the origin al 3◊3 window is chosen and midpoint of minimum and maximum values of already processed pixels is replaced with the centre pixel. experimental evaluation using MATLAB reveals that our MAUTMPF shows better performance compared to the previous de-noising algorithms in terms of Peak Signal-to-Noise Ratio ( PSNR) and Mean Square Error (MSE) for noise densities up to 90%. The validity of the proposed algorithm is v erified by testing it for different gray scale imag es.

A Decision based Unsymmetrical Trimmed Variants for the Removal of High Density Salt and Pepper Noise

A Decision based scheme using a improved mesh based snake like sorting is proposed for the restoration of gray scale images that are heavily corrupted by salt and pepper noise. The proposed algorithm uses modified mean or median for image restoration. The processed pixel is examined for 0 or 255; if checked pixel is equal to 0 or 255, then it is considered as noisy pixel else not noisy. The noisy pixel is replaced by median of unsymmetrical trimmed output, if the current window has at least three noisy pixels or mean of first and last values of the un-symmetrical trimmed output. The uncorrupted pixel is left unchanged. The proposed algorithm shows excellent noise suppression capability with good edge preservation in heavy noisy conditions both qualitative and quantitatively. The proposed algorithm was applied on various grayscale images and found to have excellent PSNR and high IEF , low MSE and consumes less time even at very high noise densities with edge preservation.

Behavior of morphological associative memories with true-color image patterns

Morphological associative memories (MAMs) are a special type of associative memory which exhibit optimal absolute storage capacity and one-step convergence. This associative model substitutes the additions and multiplications used by other models by computing maximums and minimums. This type of associative model has been applied to different pattern recognition problems including face localization and gray scale image restoration. Despite of his power, MAMs have not been applied in problems that involve true-color patterns. In this paper it is described how a MAM can be applied in problems involving true-color patterns. Furthermore, a complete study of the behavior of this associative model in the restoration of true-color images is performed using a benchmark of 14 400 images altered by different type of noises.

Image restoration using the Q-Ising spin glass.

We investigate static and dynamic properties of gray-scale image restoration by making use of the Q-Ising spin glass model, whose ladder symmetry allows us to take in account the distance between two spins. We thus give an explicit expression of the Hamming distance between the original and restored images as a function of the hyperparameters in the mean field limit. Finally, numerical simulations for real-world pictures are carried out to prove the efficiency of our model.