A lightweight multi-weather image restoration via conflict-aware prompt learning

Satellites have many high-, medium-, and low-frequency micro vibration sources that lead to the optical axis jitter of the optical load and subsequently degrade the remote sensing image quality. To address this problem, this paper developed an image motion detection and restoration method based on an inertial reference laser, and describe edits principle and key components. To verify the feasibility and performance of this method, this paper also built an image motion measurement and restoration system based on an inertial reference laser, which comprised a camera (including the inertial reference laser unit and a Hartmann wavefront sensor), an integrating sphere, a simulated image target, a parallel light pope, a vibration isolation platform, a vibration generator, and a 6 degrees of freedom platform. The image restoration principle was also described. The background noise in the experiment environment was measured, and an image motion measurement accuracy experiment was performed. Verification experiments of image restoration were also conducted under various working conditions. The experiment results showed that the error of image motion detection based on the inertial reference laser was less than 0.12 pixels (root mean square). By using image motion data to improve image quality, the modulation transfer function (MTF) of the restored image was increased to 1.61–1.88 times that of the original image MTF. The image motion data could be used as feedback to the fast steering mirror to compensate for the satellite jitter in real time and to directly obtain high-quality images.

: A dual channel infrared scanning, display, and recording system originally developed for experiments in atmospheric imaging and image restoration at 10.6 microns has been modified for use with extended targets. Two image formats are now available, one providing two spatially separable images having 40 x 40 element resolution at 200 frames per second and the other, two images having 64 x 64 element resolution at 60 frames per second which may be joined, if desired, to scan a single rectangular image. Visual CRT displays of the infrared images are provided and magnetic tape recordings are made which may be played back later at recording speed or at reduced speed for viewing and to provide computer input for data analysis. This report describes in detail the modifications made to the scanning, display, and recording system and, together with the original report published previously, provides complete documentation for the system. A separate playback, display, and computer interface system, compatible with the scanner system, to be used for image restoration processing has also been developed. Both systems are to be delivered to RADC for infrared imaging and image restoration studies.

A new background estimation method is proposed to improve iterative restoration of microscope images. The method is based on a sequence of subtractions performed in the beginning of an iterative restoration procedure. A series of experiments using background intensity detection methods were carried out in order to analyse the influence of a correct estimation of the background. The restoration results as a function of the detected background value is discussed. The results indicates a strong influence of the estimate on the restoration of images. When compared to an optimal error-based method, the proposed method was faster and obtained a good approximation to the correct value.

Visibility in underwater images is usually poor because of the presence of impurities and light being absorbed and scattered when traveling through the impure water. In this paper, TURBID image datasets are used to undergo image enhancement and restoration. TURBID datasets consist of three different types of underwater image conditions where the water solution is added with blue solution, milk solution, and chlorophyll solution. Then, the images undergo Histogram equalization (HE) and Wiener filter respectively for image enhancement and image restoration. HE as the chosen enhancement method proved that it could enhance the image quality as the water surface can be seen clearer after enhancement. Wiener Filter Class 3 is chosen as a restoration method to reduce the mean square error (MSE) value and to get a high Peak signal-to-noise ratio (PSNR) with desired SNR value. Finally, these two image processing technique, i.e., enhancement, and restoration are combined and then all the quantitative values are compared to show the image quality and clarity can be improved with the two processing techniques.

DAResNet Based on double-layer residual block for restoring industrial blurred images

NG-RED:Nonconvex group-matrix residual denoising learning for image restoration

Poor visibility in underwater images is commonly attributed to the presence of impurities and the absorbed light being scattered while travelling through impure water. In this paper, image enhancement and restoration techniques are applied to TURBID image datasets. The TURBID dataset consists of three different types of underwater image conditions where blue solution, milk solution, or chlorophyll solution is added to water. The images undergo Histogram equalization (HE) and are filtered with a Wiener filter for image enhancement and image restoration, respectively. HE as the chosen enhancement method proved that it could enhance the image quality as the water surface can be seen clearer after enhancement by visual inspection. Three Wiener filter classes are chosen as the restoration method to reduce the Mean Square Error (MSE) value and to get high Peak Signal-to-Noise-Ratio (PSNR) with desired SNR value. Finally, these two image processing techniques, enhancement, and restoration are combined and the image quantitative values are compared to show that the image performance can be improved with combined enhancement and restoration techniques. It is found that Class 1 Wiener Filter with Enhance then Restore (ER) method has a value of 0 for MSE which is the lowest compared to other studied methods and has infinity values for PSNR and SNR.

In this digest, we present a novel real-time digital zooming method based on directionally adaptive image interpolation and restoration. The proposed method first estimates an edge direction using steerable filters and performs weighted smoothing along the estimated edge direction. Bi-cubic and bi-linear interpolations are selectively used according to the estimated edge direction. Degradation and artifacts caused by interpolation are removed by employing a directionally adaptive truncated constrained least squares (TCLS) filter. The proposed digital zooming method followed by image restoration provides high-quality magnified images which are similar to the result of computationally intensive super-resolution algorithms. The proposed method can be applied to real-time image processing, and embedded in the form of the finite impulse response (FIR) filtering structure. It is suitable for digital zooming system of mobile phone cameras, tablet PCs, and digital camcorders.

Variable exponent functionals in image restoration

With the rapid development of image processing technology, the application range of image recognition technology is becoming more and more extensive. Processing, analyzing, and repairing graphics and images through computer and big data technology are the main methods to obtain image data and repair image data in complex environment. Facing the low quality of image information in the process of sports, this paper proposes to remove the noise data and repair the image based on the partial differential equation system in image recognition technology. Firstly, image recognition technology is used to track and obtain the image information in the process of sports, and the fourth-order partial differential equation is used to optimize and process the image. Finally, aiming at the problem of low image quality and blur in the transmission process, denoising is carried out, and image restoration is studied by using the adaptive diffusion function in partial differential equation. The results show that the research content of this paper greatly improves the problems of blurred image and poor quality in the process of sports and realizes the function of automatically tracking the target of sports image. In the image restoration link, it can achieve the standard repair effect and reduce the repair time. The research content of this paper is effective and applicable to image processing and restoration.

We represent the image noise reduction and restoration problems as context-dependent graphs and propose algorithms to derive the optimal graphs by the alternating direction method of multipliers (ADMM) method. An image is spatially decomposed into smooth regions and singular regions, consisting of edges and textures. The graph representing a smooth region is defined in the image domain, while that representing a singular region is defined in the wavelet domain. The optimal graphs are formulated as the solutions of constrained optimization problems over sparse graphs, where the sparseness is imposed on the edges. The graphs on the wavelet domain are solved in a hierarchical layer structure. The convergence and complexity of the algorithms have been studied. Simulation experiments demonstrate that the results of our algorithms are superior to the state-of-the-art algorithms for image noise reduction and restoration.

We study a functional with variable exponent, $1\leq p(x)\leq 2$, which provides a model for image denoising, enhancement, and restoration. The diffusion resulting from the proposed model is a combination of total variation (TV)-based regularization and Gaussian smoothing. The existence, uniqueness, and long-time behavior of the proposed model are established. Experimental results illustrate the effectiveness of the model in image restoration.

Race, Gender and Image Restoration Theory: How Digital Media Change the Landscape explores themes that are relevant to the socio-political landscape of twenty-first-century America, including race and gender representation, social media and traditional media framing, and image restoration management. This book provides a comprehensive discussion of Critical Race Theory (CRT) and Image Restoration Theory (IRT) to establish a baseline for a conversation on celebrity image restoration tactics used on social media platforms such as Twitter and Facebook as well as traditional media platforms. Case studies offer a broad overview of politics, sports and entertainment image management and restoration. Recommended for scholars interested in public relations, crisis management, Image Repair Theory (IRT), and representations of race and gender in mass media.

In this paper, we apply the so-called Q'tron NN (neural network) paradigm to perform image halftoning and the 'associated' image restoration. These processes are considered to be located at the different sides of the same process. On one side, the process converts a grey-tone image into a binary image, i.e., halftoning. On the other side, the process just performs the inverse, i.e., it restores a binary image to a grey-tone image. One will see that such an auto-associativity regarding the two tightly correlated images is one of the important features of a Q'tron NN. Experimental results are presented to demonstrate that the resulting quality of images is quite satisfactory.

Several powerful, but heuristic techniques in recent image denoising literature have used multiple (typically overcomplete) image representations. This paper presents a framework for multiple-domain image modeling and restoration, based on fundamental statistical estimation principles. Information about image attributes from multiple wavelet transforms is incorporated as moment constraints on the underlying image prior. Our method constructs the maximum entropy distribution consistent with these moment constraints. A maximum a posteriori probability (MAP) image restoration algorithm based on this maximum entropy prior is developed. Unlike previous multiple-domain algorithms, ours satisfies certain desirable optimality properties and provides an information-theoretic figure of merit for the choice of domains. Simulation results show that the estimator is vastly superior to single-domain image restoration both in terms of mean squared error and perceptual quality.