- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Underwater Imaging Based on LF and Polarization
摘要： The underwater image restoration based on polarization information has achieved good results in improving the image quality in scattering media. However, the previous methods are difficult to obtain the true distribution of degree of polarization in the scene. In this paper, we combine synthetic aperture imaging with polarimetric imaging, and propose a method for retrieving radiation of object based on the degree of polarization and intensity of backscattering at the multi-view image. In addition, compared with the previous methods, the proposed method can achieve simultaneous acquisition of 4D light field information and polarization information, effectively increasing the information dimension obtained by single imaging. In order to verify the effectiveness and superiority of the proposed method, we have established a relevant experimental platform and compared with the experimental results of the previous methods, and obtained the expected experimental results.
关键词： synthetic aperture imaging,polarimetric imaging,underwater image restoration
更新于2025-09-23 15:23:52
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Iterative Adaptive Nonconvex Low-Rank Tensor Approximation to Image Restoration Based on ADMM
摘要： In this paper, in order to recover more ?ner details of the image and to avoid the loss of image structure information for image restoration problem, we develop an iterative adaptive weighted core tensor thresholding (IAWCTT) approach based on the alternating direction method of multipliers (ADMM). By observing the decoupling property of the ADMM algorithm, we ?rst propose that the key step to image restoration is to tackle the denoising subproblem ef?ciently using appropriate prior information. Secondly, by analyzing the properties of the core tensor, we propose that low-rank tensor approximation can be implemented by penalizing the core tensor itself, instead of penalizing the CP rank, Tucker rank or the multilinear rank and Tubal rank. The IAWCTT approach is proposed to solve the denoising subproblem in the ADMM framework, and we claim that such an adaptive weighted scheme is equivalent to a kind of nonconvex penalty for the core tensor; thus, it is unnecessary to use the nonconvex penalty term to induce strong sparse/low-rank solution in image restoration optimization problem, because the scheme that selecting appropriate weights to the convex penalty term can also lead to strong sparse/low-rank solution. Numerical experiments show that our proposed model and algorithm are comparable to other state-of-the-art models and methods.
关键词： Image restoration,Low-rank tensor approximation,ADMM,Nonconvex penalty
更新于2025-09-23 15:23:52
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Research on Spatially Adaptive High-Order Total Variation Model for Weak Fluorescence Image Restoration
摘要： Confocal laser scanning microscopy (CLSM) has emerged as one of the most advanced ?uorescence cell imaging techniques in the ?eld of biomedicine. However, ?uorescence cell imaging is limited by spatial blur and additive white noise induced by the excitation light. In this paper, a spatially adaptive high-order total variation (SA-HOTV) model for weak ?uorescence image restoration is proposed to conduct image restoration. The method consists of two steps: optimizing the deconvolution model of the ?uorescence image by the generalized Lagrange equation and alternating direction method of multipliers (ADMM); using spatially adaptive parameters to balance the image ?delity and the staircase e?ect. Finally, an comparison of SA-HOTV model and Richardson-Lucy model with total variation (RL-TV model) indicates that the proposed method can preserve the image details ultimately, reduce the staircase e?ect substantially and further upgrade the quality of the restored weak ?uorescence image.
关键词： weak ?uorescence,spatially adaptive high-order total variation (SA-HOTV),image restoration,confocal microscopy
更新于2025-09-23 15:23:52
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On image restoration from random sampling noisy frequency data with regularization
摘要： Consider the image restoration using random sampling noisy frequency data by total variation regularization. By exploring image sparsity property under wavelet expansion, we establish an optimization model with two regularizing terms specifying image sparsity and edge preservation on the restored image. The choice strategy for the regularizing parameters is rigorously set up together with corresponding error estimate on the restored image. The cost functional with data-fitting in the frequency domain is minimized using the Bregman iteration scheme. By deriving the gradient of the cost functional explicitly, the minimizer of the cost functional at each Bregman step is also generated by an inner iteration process with Tikhonov regularization, which is implemented stably and efficiently due to the special structure of the regularizing iterative matrix. Numerical tests are given to show the validity of the proposed scheme.
关键词： Image restoration,iteration,numerics,total variation,wavelet sparsity,error estimate
更新于2025-09-23 15:23:52
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Space-variant generalised Gaussian regularisation for image restoration
摘要： We propose a new space-variant regularisation term for variational image restoration based on the assumption that the gradient magnitudes of the target image distribute locally according to a half-Generalised Gaussian distribution. This leads to a highly ?exible regulariser characterised by two per-pixel free parameters, which are automatically estimated from the observed image. The proposed regulariser is coupled with either the L2 or the L1 ?delity terms, in order to e?ectively deal with additive white Gaussian noise or impulsive noises such as, e.g. additive white Laplace and salt and pepper noise. The restored image is e?ciently computed by means of an iterative numerical algorithm based on the alternating direction method of multipliers. Numerical examples indicate that the proposed regulariser holds the potential for achieving high-quality restorations for a wide range of target images characterised by di?erent gradient distributions and for the di?erent types of noise considered.
关键词： alternating direction method of multipliers,half-Generalised Gaussian distribution,Image restoration,variational methods
更新于2025-09-23 15:23:52
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Research on Image Restoration Algorithms Based on BP Neural Network
摘要： With the development of information transmission technology and computer technology, information acquisition mode is mainly converted from character to image nowadays. However, in the process of acquiring and transmitting images, image damage and quality decrease due to various factors. Therefore, how to restore image has become a research hotspot in the field of image processing. This paper establishes an image restoration model based on BP neural network. The simulation results show that the proposed method has made a great improvement compared with the traditional image restoration method.
关键词： image processing,BP neural network,image restoration,image denoising
更新于2025-09-23 15:22:29
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Offshore Underwater Image Restoration Using Reflection-Decomposition-Based Transmission Map Estimation
摘要： A new restoration model for underwater images is presented, based on the dark channel reflection-illumination decomposition and local backscattering lighting estimation, to promote the clarity of edge detail and the colorfulness of the output image. For turbid offshore environments, a novel underwater image restoration method is further introduced by applying a statistical prior to the offshore attenuation coefficients. It is shown that the estimated transmission map with lighting-reflection decomposition, rather than dividing the dark channel by the maximal backscattering light as the other methods do, provides better clarity and color restoration. Detailed qualitative and quantitative analysis on hundreds of underwater images is performed, which demonstrates that the proposed method outperforms the state-of-the-art algorithms on the images taken in offshore water characterized by a heavy concentration of colored dissolved organic matter and total suspended matter, and is suitable for fast underwater processing.
关键词： underwater image restoration,underwater imaging,Offshore,underwater optical model
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM) - Sheffield (2018.7.8-2018.7.11)] 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM) - Restoration of Multilayered Single-Photon 3D Lidar Images
摘要： This paper proposes a new algorithm to restore 3D single-photon Lidar images obtained under challenging realistic scenarios which include imaging multilayered targets such as semi-transparent surfaces or imaging through obscurants such as scattering media (e.g., water, fog). The Data restoration and exploitation is achieved by minimising an appropriate cost-function accounting for the data Poisson statistics and the available prior knowledge regarding the depth and reflectivity estimates. The proposed algorithm takes into account (i) the non-local spatial correlations between pixels, by using a convex non-local total variation (TV) regularizer, and (ii) the clustered nature of the returned photons, by using a collaborative sparse prior. The resulting minimization problem is solved using the alternating direction method of multipliers (ADMM) that offers good convergence properties. The algorithm is validated using both synthetic and real data which show the benefit of the proposed strategy in the sparse regime due to a fast acquisition or in presence of a high background due to obscurants.
关键词： Poisson statistics,collaborative sparsity,ADMM,image restoration,NR3D,Lidar waveform
更新于2025-09-23 15:22:29
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Visual-Quality-Driven Learning for Underwater Vision Enhancement
摘要： The image processing community has witnessed remarkable advances in enhancing and restoring images. Nevertheless, restoring the visual quality of underwater images remains a great challenge. End-to-end frameworks might fail to enhance the visual quality of underwater images since in several scenarios it is not feasible to provide the ground truth of the scene radiance. In this work, we propose a CNN-based approach that does not require ground truth data since it uses a set of image quality metrics to guide the restoration learning process. The experiments showed that our method improved the visual quality of underwater images preserving their edges and also performed well considering the UCIQE metric.
关键词： Image Restoration,Underwater Vision,Deep Learning,Image Quality Metrics
更新于2025-09-23 15:21:21
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[IEEE 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) - Las Vegas, NV (2018.4.8-2018.4.10)] 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) - Underwater Image Restoration using Deep Networks to Estimate Background Light and Scene Depth
摘要： Images taken underwater often suffer color distortion and low contrast because of light scattering and absorption. An underwater image can be modeled as a blend of a clear image and a background light, with the relative amounts of each determined by the depth from the camera. In this paper, we propose two neural network structures to estimate background light and scene depth, to restore underwater images. Experimental results on synthetic and real underwater images demonstrate the effectiveness of the proposed method.
关键词： depth estimation,image restoration,convolutional neural networks,Underwater images
更新于2025-09-23 15:21:21