Image Restoration Based on Parallel GA and Hopfield NN

This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with low-complexity, expressed in the wavelet coefficients, taking advantage of the well known sparsity of wavelet representations. Previous works have investigated wavelet-based restoration but, except for certain special cases, the resulting criteria are solved approximately or require demanding optimization methods. The EM algorithm herein proposed combines the efficient image representation offered by the discrete wavelet transform (DWT) with the diagonalization of the convolution operator obtained in the Fourier domain. Thus, it is a general-purpose approach to wavelet-based image restoration with computational complexity comparable to that of standard wavelet denoising schemes or of frequency domain deconvolution methods. The algorithm alternates between an E-step based on the fast Fourier transform (FFT) and a DWT-based M-step, resulting in an efficient iterative process requiring O(N log N) operations per iteration. The convergence behavior of the algorithm is investigated, and it is shown that under mild conditions the algorithm converges to a globally optimal restoration. Moreover, our new approach performs competitively with, in some cases better than, the best existing methods in benchmark tests.

The expectation-maximization (EM) algorithm for maximum-likelihood image recovery is guaranteed to converge, but it converges slowly. Its ordered-subset version (OS-EM) is used widely in tomographic image reconstruction because of its order-of-magnitude acceleration compared with the EM algorithm, but it does not guarantee convergence. Recently the ordered-subset, separable-paraboloidal-surrogate (OS-SPS) algorithm with relaxation has been shown to converge to the optimal point while providing fast convergence. We adapt the relaxed OS-SPS algorithm to the problem of image restoration. Because data acquisition in image restoration is different from that in tomography, we employ a different strategy for choosing subsets, using pixel locations rather than projection angles. Simulation results show that the relaxed OS-SPS algorithm can provide an order-of-magnitude acceleration over the EM algorithm for image restoration. This new algorithm now provides the speed and guaranteed convergence necessary for efficient image restoration.

The expectation-maximization (EM) algorithm for maximum-likelihood image recovery converges very slowly. Thus, the ordered subsets EM (OS-EM) algorithm has been widely used in image reconstruction for tomography due to an order-of-magnitude acceleration over the EM algorithm. However, OS-EM is not guaranteed to converge. The recently proposed ordered subsets, separable paraboloidal surrogates (OS-SPS) algorithm with relaxation has been shown to converge to the optimal point while providing fast convergence. In this paper, we develop a relaxed OS-SPS algorithm for image restoration. Because data acquisition is different in image restoration than in tomography, we adapt a different strategy for choosing subsets in image restoration which uses pixel location rather than projection angles. Simulation results show that the order-of-magnitude acceleration of the relaxed OS-SPS algorithm can be achieved in restoration. Thus the speed and the guarantee of the convergence of the OS algorithm is advantageous for image restoration as well.

A rapid hybrid algorithm for image restoration combining parametric Wiener filtering and wave atom transform

To improve data transmission efficiency, image compression is a commonly used method with the disadvantage of accompanying image distortion. There are many image restoration (IR) algorithms, and one of the most advanced algorithms is the generative adversarial network (GAN)-based method with a high correlation to the human visual system (HVS). To evaluate the performance of GAN-based IR algorithms, we proposed an ensemble image quality assessment (IQA) called ATDIQA (Auxiliary Transformer with DISTS IQA) to give weights on multiscale features global self-attention transformers and local features of convolutional neural network (CNN) IQA of DISTS. The result not only performed better on the perceptual image processing algorithms (PIPAL) dataset with images by GAN IR algorithms but also has good model generalization over LIVE and TID2013 as traditional distorted image datasets. The ATDIQA ensemble successfully demonstrates its performance with a high correlation with the human judgment score of distorted images.

Summary form only given. This article gives a brief overview of theoretical advances, computing trends, applications and future perspectives in parallel genetic algorithms. It explains basic terms and behavior of (parallel) genetic algorithms. Genetic algorithms are easily parallelized algorithms, therefore two kinds of possible parallelism, data parallelism and control parallelism, are mentioned and described towards them. Parallelism of genetic algorithms brings many advantages and gains. Classifications of these algorithms are often based on the type of computing model, a walk strategy and the used computing machinery. Afterwards significant milestones in the theory with latest advances are briefly mentioned. Then current trends in parallel computing with stress computer architectures of parallel systems, interconnection topologies, operating systems, parallel (genetic) libraries and programming paradigms are reviewed shortly. The sufficient space is devoted to the latest applications of parallel genetic algorithms. After the discussion section, perspectives of the algorithms are predicted till the year 2005. The information in the article is segregated into two periods before and after the year 2000 in all chapters. The second period is more interesting and of higher importance, because it highlights recent research efforts and gives some hints about possible future trends. That is why we devote much space to the second period. As there is no such an overview of the recent period of parallel genetic algorithms, our investigation could be appealing and useful in many aspects.

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In this paper, based on the modified Hopfield neural network, we present two variational PDEs (Partial Differential Equations) as the regularization terms to the image restoration model. One is based on a harmonic model and the other is based on a total variation model. Then, we propose two novel variational image restoration algorithms based on the Modified Hopfield Neural Network (MHNN). Both algorithms are aiming to restore the degraded images and preserve the edges with improved visual quality. The experimental results demonstrate that our proposed algorithms perform better than other known neural network based restoration algorithms.

Region extraction and labeling is a fundamental process in building computer systems that automatically analyze and interpret images and image sequences of a scene. It involves extracting meaningful regions and their attributes from an image that are of interest in a given domain, e.g., a human face in face recognition and surveillance applications, a vehicle in intelligent highway monitoring, or an industrial tool in automated manufacturing and inspection applications. In this paper, we present a generalized and fast region labeling algorithm that can be applied to numerous image processing tasks: region extraction in binary images, region segmentation from gray scale or color images, region boundary extraction, chain code extraction, run-length coding of images, and image restoration from chain codes. The general applicability of our algorithm stems from its strengths in identifying the fundamental and common computational steps and providing robust and efficient procedures to accomplish these steps without requiring multiple scans of images. Our algorithm is shown to be more effective in terms of space and computational efficiency, in comparison with many existing approaches to region labeling and extraction. Results from our experiments with over 5,000 frames obtained from MPEG-1 video bitstreams demonstrate the good performance of our algorithm in terms of video text segmentation accuracy and computational efficiency.

We present a tutorial review of recent developments in restoring images that are degraded by both blur and noise. We consider three fundamental aspects of digital image restoration: modeling, identification algorithms, and restoration algorithms. An overview of modeling the degradations, and certain properties of images are given first. We then survey the methods that identify these models. Image restoration algorithms are surveyed in two categories: general algorithms and specialized algorithms. We briefly discuss present and future research topics in the field. Our emphasis here is on fundamental concepts and ideas rather than mathematical details.

Underwater laser imaging is of great significance in underwater search and marine science, <i>etc</i>. However, traditional underwater laser imaging is often of poor quality with noises and blurs, moreover, the resolution of the image is also low. In order to obtain clear underwater images with high resolution and quality, here, we have designed a range gated imaging underwater imaging system and realized an image restoration approach. In this paper, based on the introduction to the imaging system and image restoration algorithm, the experiment is established by setting the imaging system under water in the lake to capture the underwater targets. With the proposed underwater image restoration approach, images of high quality could be retrieved which proves that the method is able to identify the target ~10 meters away underwater.

Human Pose Estimation (HPE) models have varied applications and represent a cutting-edge branch of study, whose systems such as MediaPipe (MP), OpenPose (OP), and AlphaPose (ALP) show marked success. One of these areas, however, that is inadequately researched is the impact of image degradation on the accuracy of HPE models. Image degradation refers to images whose visual quality has been purposefully degraded by means of techniques, such as brightness adjustments (which can lead to an increase or a decrease in the intensity levels), geometric rotations, or resolution downscaling. The study of how these types of degradation impact the performance functionality of HPE models is an under-researched domaina that is a virtually unexplored area. In addition, current methods of the efficacy of existing image restoration techniques have not been rigorously evaluated and improving degraded images to a high quality has not been well examined in relation to improving HPE models. In this study, we explicitly clearly demonstrate a decline in the precision of the HPE model when image quality is degraded. Our qualitative and quantitative measurements identify a wide difference in performance in identifying landmarks as images undergo changes in brightness, rotation, or reductions in resolution. Additionally, we have tested a variety of existing image enhancement methods in an attempt to enhance their capability in restoring low-quality images, hence supporting improved functionality of HPE. Interestingly, for rotated images, using Pillow of OpenCV improves landmark recognition precision drastically, nearly restoring it to levels we see in high-quality images. In instances of brightness variation and in low-quality images, however, existing methods of enhancement fail to yield the improvements anticipated, highlighting a large direction of study that warrants further investigation and calls for additional research. In this regard, we proposed a wide-ranging system for classifying different types of image degradation systematically and for selecting appropriate algorithms for image restoration, in an effort to restore image quality. A key finding is that in a related study of current methods, the Tuned RotNet model achieves 92.04% accuracy, significantly outperforming the baseline model and surpassing the official RotNet model in predicting rotation degree of images, where the accuracy of official RotNet and Tuned RotNet classifiers were 61.59% and 92.04%, respectively. Furthermore, in an effort to facilitate future research and make it easier for other studies, we provide a new dataset of reference images and corresponding degenerated images, addressing a notable gap in controlled comparative studies, since currently there is a lack of controlled comparatives.

Aiming at correcting the severe chromatic aberration of the long focal lens, the traditional optical design often requires employing complex structure, introducing special dispersive glasses or even utilizing the hybrid refractive-diffractive imaging method. However, complex structure will bring several drawbacks such as large volume and heavy weight. Special glasses and refractive-diffractive hybrid imaging will greatly increase the cost, which hardly meets the needs of miniaturization and low cost of optical imaging module. Although image restoration algorithm is commonly used to optimize the image quality to a certain extent, the optical design and image restoration process are independent of each other. Therefore, it is difficult to ensure the high resolution of the image while realizing the light weight, small volume and low cost of the optical system simultaneously. Utilizing the computational imaging theory, a simple long-focus optical system design method based on the optical/image co-design is proposed and deomonstrated in this paper. On the basis of the idea of global optimization, the reported approach considers the two independent links of optical design and image restoration as a combination. The imaging quality requirements in the optical system design are relaxed at the front end, and the image restoration algorithm is used to remove the residual aberrations in the back end. This method can not only obtain the same or even higher imaging performance, but also reduce the complexity of the optical system.

The recent algorithm ForWaRD based on the two steps: (i) the Fourier domain deblurring and (ii) wavelet domain denoising, shows better restoration results than those using traditional image restoration methods. In this paper, we study other deblurring schemes in ForWaRD and demonstrate such two-step approach is effective for image restoration.

The remote-sensed images may be degraded by some factors during their obtaining. It decreases the quality of the images and becomes an obstacle of the image recognition. So it's very necessary to restore the degraded image. At present, most of the image restoration algorithms are depended on the priori knowledge or a given physical model of the image degeneration. The paper presents a new algorithm for remote-sensed image restoration based on gene expression programming (GEP). The paper needs a reference image at first; then applies the GEP to mine the math function between reference image and degraded image; at last, restores the degraded image with the math function. The experiment results demonstrate that the method is effective and practical.