Effective Image Restoration Research Articles

Rank minimization via adaptive hybrid norm for image restoration

Rank minimization methods have achieved promising performance in various image processing tasks. However, there are still two challenging problems in the existing works. One is that most of the current methods only regularize singular values by using a single l1-norm, such as the well-known nuclear norm minimization (NNM) and the weighted nuclear norm minimization (WNNM). Consequently, many small singular values are shrunk to zero, which is unbeneficial for restoring image details. The other is that how to adaptively evaluate the importance of each singular value is still a suspending problem. In this paper, we propose a novel rank minimization method, namely adaptive hybrid norm minimization (AHNM) model, to solve the above problems. Specifically, for each singular value, we employ l2-norm to compensate for l1-norm, and introduce a significance factor to assess its importance adaptively. More importantly, we show that closed-form solutions for all subproblems can be derived simply by using alternating optimization. With the aid of the proposed AHNM model, we further develop a general yet effective image restoration algorithm based on the nonlocal self-similarity (NSS) of images. Numerous experimental results demonstrate that the proposed AHNM model consistently outperforms many state-of-the-art restoration methods, including model-based methods and deep learning-based methods.

Improvement of Point Spread Function Estimation Method for Motion Blurred Image and Image Restoration

The estimation of point spread function (PSF) of motion-blurred images is the key to image restoration, which is mainly affected by two physical quantities: blur angle and blur length. The traditional point spread function estimation method is easy to be disturbed by the bright cross in the centre of the blurred image spectrum when estimating the fuzzy angle, which affects the quality of image restoration. In addition, when using the traditional Radon transform to detect fuzzy angles, the area of bright spots in the picture is large, which will cause large fluctuations in the detection results. Therefore, this paper proposes a cepstrum analysis method for motion blurred images. Firstly, the gradient cepstrum estimation of the blurred image is given, and the cepstrum is binarized and further refined by setting the gray threshold of the cepstrum. Finally, the line fitting is performed on the refined binary image, and the slope of the fitting line is calculated, so that the point spread function of the blurred image can be accurately and directly estimated, and better image restoration effect can be obtained.

Study on the image restoration strategy and effect of corporates in crisis situations based on text mining

A corporate's reputation may be significantly impacted by how it handles emergencies. To investigate the effect of different coping strategies on corporate image restoration, we collected 47 representative events of corporates in different industries as well as relevant comments from Weibo. Based on the image restoration theory proposed by Benoit, the effects of different strategies on corporate reputation restoration were analyzed by using sentiment analysis and the topic model. We found that the two strategies of corrective action and mortification can usually better alleviate the negative emotions of the public. On the contrary, if corporates respond with strategies of denial, evading responsibility, or reducing offensiveness, there could be strong feelings of dissatisfaction. The case study of the food industry also finds that even if the effect of alleviating the negative emotions of the public is not significant, an appropriate response could shift the attention of some consumers and to some extent serve the purpose of restoring the corporate reputation.

Lucky imaging method based on wavelet analysis

ABSTRACT Lucky imaging is able to reconstruct high-resolution images effectively from astronomical images affected by atmospheric turbulence. Its image reconstruction process can be carried out in the spatial domain or in the Fourier domain. This paper extend the lucky imaging technique to the wavelet domain, and proposes a lucky imaging method based on wavelet analysis. First, the short-exposure astronomical images are decomposed into four subbands by using the bior3.7 wavelet basis. Then, for the low-frequency subbands, the components with higher wavelet coefficients are extracted in a given selection ratio for fusion. For the high-frequency subbands in the horizontal, vertical, and diagonal directions, taking the whole subband as a unit, the subbands with smaller sums of wavelet coefficients are extracted for stacking in a given selection ratio. Finally, an inverse wavelet transform is performed on the four fused subbands to reconstruct the resultant image. Experimental results show that the proposed algorithm can better extract the details hidden in the original short-exposure astronomical images and obtain a higher-resolution image, and that it has a good image restoration effect.

Image restoration via exponential scale mixture‐based simultaneous sparse prior

Image prior plays a decisive role in the performance of widely studied model-based restoration methods. To further improve restoration performance, this paper proposes an exponential scale mixture-based simultaneous sparse prior (ESM-SSP) to accurately characterize image prior information. Specifically, first, two structured dictionaries are adaptively learned to explore the local and non-local sparsity of similar patch groups simultaneously. Then, the exponential scale mixture (ESM) is employed to model simultaneous sparse coefficients. The adoption of ESM enables us to accurately estimate simultaneous sparse coefficients by adaptively adjusting the regularization parameters. With the aid of ESM-SSP, an effective image restoration algorithm is developed to preserve more image details. Extensive experimental results on image denoising and deblocking demonstrate that compared with many state-of-the-art model-based methods, the proposed ESM-SSP-based restoration algorithm not only has competitive peak signal-to-noise ratio, but also produces higher structural similarity index and better visuals. More importantly, the proposed method can also compete favourably with the superior deep learning-based restoration methods.

An Image Compression Encryption Algorithm Based on Chaos and ZUC Stream Cipher.

In order to improve the transmission efficiency and security of image encryption, we combined a ZUC stream cipher and chaotic compressed sensing to perform image encryption. The parallel compressed sensing method is adopted to ensure the encryption and decryption efficiency. The ZUC stream cipher is used to sample the one-dimensional chaotic map to reduce the correlation between elements and improve the randomness of the chaotic sequence. The compressed sensing measurement matrix is constructed by using the sampled chaotic sequence to improve the image restoration effect. In order to reduce the block effect after the parallel compressed sensing operation, we also propose a method of a random block of images. Simulation analysis shows that the algorithm demonstrated better encryption and compression performance.

Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning.

It is important to study the evaluation algorithm for the stroke rehabilitation treatment effect to make accurate evaluation and optimize the stroke disease treatment plan according to the evaluation results. To address the problems of poor restoration effect of positron emission tomography (PET) image and recognition restoration effect of evaluation data and so on. In the paper, we propose a stroke rehabilitation treatment effect evaluation algorithm based on cross-modal deep learning. Magnetic resonance images (MRI) and PET of stroke patients were collected as evaluation data to construct a multimodal evaluation dataset, and the data were divided into positive samples and negative samples. According to the mapping relationship between MRI and PET, three-dimensional cyclic adversarial is used to generate the neural network model to recover the missing PET data. Using the cross-modal depth learning network model, the RGB image, depth image, gray image, and normal images of MRI and PET are taken as the feature images and the multifeature fusion method is used to fuse the feature images, output the recognition results of MRI and PET, and evaluate the effect of stroke rehabilitation treatment according to the recognition results. The results show that the proposed algorithm can accurately restore PET images, the evaluation data recognition effect is good, and the evaluation data recognition accuracy is higher than 95%. The evaluation accuracy of stroke rehabilitation treatment effect is high, the evaluation time varies between 0.56 s and 0.91 s, and the practical application effect is good.

Single underwater image restoration based on descattering and color correction

Underwater images typically exhibit color distortion and low contrast due to the absorption and scattering of the non-uniform medium during the propagation of light underwater. To address these problems, an effective underwater image restoration method is presented in this paper. Firstly, we adopted an adaptive dark channel prior that can be applied to underwater images with different color tones. Then, the local water light with strong robustness is obtained by utilizing maximum filtering and Gaussian low-pass filtering. Additionally, a linear relationship between blurred and clear images in the dark channel was introduced combining with the attenuation coefficient compensation to estimate the transmission of r, g and b channels. Ultimately, we designed a new adaptive color compensation method by taking into account the attenuation difference of the three color channels. Simulation and experimental results show that the proposed method can effectively improve the clarity and contrast of the image and eliminate color distortion.

Quaternion-based weighted nuclear norm minimization for color image restoration

Color image restoration is one of the basic tasks in pattern recognition. Unlike grayscale image, each color image has three channels in the RGB color space. Due to the inner-relationship within the three channels, color image restoration is usually much more difficult than its grayscale counterpart. Indeed, new problems such as color artifacts could emerge when the grayscale image processing methods are extended to color images directly. Note that one of the most effective gray image restoration methods is the weighted nuclear norm minimization (WNNM) approach. However, when applied to color images, the results of WNNM are usually not as promising as that of grayscale images. In order to solve this problem, in this paper, we propose to restore color images with the quaternion-based WNNM method (QWNNM) since the structure of color channels can be well preserved with quaternion representation. The proposed model can be solved efficiently by the alternating direction method of multipliers (ADMM). The theoretical analysis of the optimal solution is also presented. Numerical experiments are carefully conducted with different kinds of degradation to illustrate the superior performance of our proposed QWNNM over the state-of-the-art methods, including a celebrated deep learning approach, in both visual quality and numerical results.

Stereo-IA: stereo visual intensity alignment and beyond under radiation variation.

Stereo vision is a hot research topic at present, but due to the radiation changes, there will be a large intensity difference between stereo pairs, which will lead to serious degradation of stereo vision based matching, pose estimation, image segmentation and other tasks. Previous methods are not robust to radiation changes or have a large amount of calculation. Accordingly, this paper proposes a new stereo intensity alignment and image enhancement method based on the latest SuperPoint features. It combines the triangle based bearings-only metric, scale-ANCC and belief propagation model and has strong robustness to radiation changes. The quantitative and qualitative comparison experiments on Middlebury datasets verify the effectiveness of the proposed method, and it has a better image restoration and matching effect under the radiation changes.

An Old Photo Image Restoration Processing Based on Deep Neural Network Structure

Old photos retain precious historical image information, but today’s existing old photos often have varying degrees of damage. Although these old photos can be digitally processed and then restored, the restoration of old photos involves multiple areas of image restoration and has various types of degradation. Currently, there is no unified model for repairing multiple types of degradation of old photos. Photo restoration technology still has a lot of developments. Traditional image restoration technology mainly repairs the missing areas of the image based on mathematical formulas or thermal diffusion. This technology can only repair images with simple structures and small damaged areas and is difficult to apply in people’s daily lives. The emergence of deep learning technology has accelerated the pace of research on image restoration. This article will discuss the methods of repairing old photos based on deep neural networks. It is aimed at proposing an image restoration method based on deep neural network to enhance the effect of image restoration of old photos and provide more possibilities for restoration of old photos. This article discusses the background significance of image restoration methods, designs an image restoration model based on deep neural networks, and introduces the structure, principle, and loss function of the model. Finally, this article did a comparative experiment to compare the model in this article with other models and draw the conclusion: in the blur repair experiment, the algorithm in this paper is better than other algorithms for the peak signal-to-noise ratio and structure similarity of the repaired image; in the damage repair experiment, the value of the algorithm’ s peak signal-to-noise ratio is 32.34, and the structure similarity under different damage average levels is 0.767, which is also higher than other algorithms. Therefore, the model in this paper has the best effect on image restoration.

A Deconvolutional Deblurring Algorithm Based on Short- and Long-Exposure Images.

An iterative image restoration algorithm, directed at the image deblurring problem and based on the concept of long- and short-exposure deblurring, was proposed under the image deconvolution framework by investigating the imaging principle and existing algorithms, thus realizing the restoration of degraded images. The effective priori side information provided by the short-exposure image was utilized to improve the accuracy of kernel estimation, and then increased the effect of image restoration. For the kernel estimation, a priori filtering non-dimensional Gaussianity measure (BID-PFNGM) regularization term was raised, and the fidelity term was corrected using short-exposure image information, thus improving the kernel estimation accuracy. For the image restoration, a P norm-constrained relative gradient regularization term constraint model was put forward, and the restoration result realizing both image edge preservation and texture restoration effects was acquired through the further processing of the model results. The experimental results prove that, in comparison with other algorithms, the proposed algorithm has a better restoration effect.

Exploiting Non-Local Priors via Self-Convolution for Highly-Efficient Image Restoration.

Constructing effective priors is critical to solving ill-posed inverse problems in image processing and computational imaging. Recent works focused on exploiting non-local similarity by grouping similar patches for image modeling, and demonstrated state-of-the-art results in many image restoration applications. However, compared to classic methods based on filtering or sparsity, non-local algorithms are more time-consuming, mainly due to the highly inefficient block matching step, i.e., distance between every pair of overlapping patches needs to be computed. In this work, we propose a novel Self-Convolution operator to exploit image non-local properties in a unified framework. We prove that the proposed Self-Convolution based formulation can generalize the commonly-used non-local modeling methods, as well as produce results equivalent to standard methods, but with much cheaper computation. Furthermore, by applying Self-Convolution, we propose an effective multi-modality image restoration scheme, which is much more efficient than conventional block matching for non-local modeling. Experimental results demonstrate that (1) Self-Convolution with fast Fourier transform implementation can significantly speed up most of the popular non-local image restoration algorithms, with two-fold to nine-fold faster block matching, and (2) the proposed online multi-modality image restoration scheme achieves superior denoising results than competing methods in both efficiency and effectiveness on RGB-NIR images. The code for this work is publicly available at https://github.com/GuoLanqing/Self-Convolution.

Multiscale Image Dehazing and Restoration: An Application for Visual Surveillance

The captured outdoor images and videos may appear blurred due to haze, fog, and bad weather conditions. Water droplets or dust particles in the atmosphere cause the light to scatter, resulting in very limited scene discernibility and deterioration in the quality of the image captured. Currently, image dehazing has gained much popularity because of its usability in a wide variety of applications. Various algorithms have been proposed to solve this ill-posed problem. These algorithms provide quite promising results in some cases, but they include undesirable artifacts and noise in haze patches in adverse cases. Some of these techniques take unrealistic processing time for high image resolution. In this paper, to achieve real-time halo-free dehazing, fast and effective single image dehazing we propose a simple but effective image restoration technique using multiple patches. It will improve the shortcomings of DCP and improve its speed and efficiency for high-resolution images. A coarse transmission map is estimated by using the minimum of different size patches. Then a cascaded fast guided filter is used to refine the transmission map. We introduce an efficient scaling technique for transmission map estimation, which gives an advantage of very low-performance degradation for a high-resolution image. For performance evaluation, quantitative, qualitative and computational time comparisons have been performed, which provide quiet faithful results in speed, quality, and reliability of handling bright surfaces.

Restoring SAR Images Using Effective Image Restoration Approach

Landsat 7 Enhanced Thematic Mapper Plus satellite images presents an important data source for many applications related to remote sensing. An effective image restoration method is proposed to fill the missing information in the satellite images. The segmentation of satellite images to find the SLIC Super pixels and then to find the image Segments. The Boundary Reconstruction is performed using Edge Matching to find the area of the missing region. Peak Signal to Noise Ratio and Root Mean Square Error using with boundary reconstruction and without boundary reconstruction to evaluate the quality and the error rate of the satellite images. The results show the capability to predict the missing values accurately in terms of quality, time without need of external information.The values for PSNR has changed from 25 to 90 and RMSE has changed from 180 to 4 in Red Channel of an image.This indicates that quality of the image is high and error rate is less.

Illumination correction with optimized kernel extreme learning machine based on improved marine predators algorithm

AbstractAiming at the problem of poor image illumination correction accuracy, a kernel extreme learning machine optimized based on the differential evolution‐improved marine predators algorithm is proposed to estimate the scene illumination information and restore the image. First, in order to solve the problem of randomization of the initial population of the marine predators algorithm, differential evolutions is used to provide a set of suitable initial populations to the algorithm. Then, an improved algorithm is used to optimize the weight and bias of the kernel extreme learning machine to solve the problem of the randomness of the weight and bias, so that the learning machine converges to the global optimal value and avoids the unstable predictions. Finally, after obtaining the predicted illumination information, the image model is corrected to the image effect under standard illumination through diagonal transformation. It can be seen from the experimental results that the best predictive value of the differential evolution marine predator algorithm learning machine is 0.0135915, and the quasi‐bias is only 0.001687. Compared with traditional illumination estimation algorithms such as random vector functional link and extreme learning machine, the differential evolution marine predator algorithm learning machine algorithm proposed in this article has better stability, higher accuracy of calculated predicted values, and better image restoration effect.

Learning domain transfer for unsupervised magnetic resonance imaging restoration and edge enhancement

AbstractRecently, many effective medical image restoration methods based on deep learning have been proposed. Most of the methods are used to solve single image processing tasks, such as image noise removal, image deblurring, and image super‐resolution. However, real medical images often suffer from multiple degradation factors, such as signal interference in the process of shooting or the relative movement of patients during the process of image acquisition. The image restoration methods only considering a single image degradation factor often fail to yield satisfactory results for the restoration purpose of practical medical images. It is difficult to obtain paired medical images to incorporate real image processing tasks into the framework of supervised learning. For the restoration of real medical images with multiple degradation factors, an unsupervised learning method based on domain transfer to resolve the MRI restoration problem is proposed. Specifically, learning invariant representations from a degraded image and a transferred image via adversarial domain adaption and then extra self‐supervised modules to further improve the robustness of representations are used. In addition, the Laplacian operator is embedded in the encoder to extract additional edges, which can form additional constraints on edge recovery. Experimental results show that the proposed method is effective in both image restoration and edge preservation.

DFBDehazeNet: an end-to-end dense feedback network for single image dehazing

The feedback mechanism method of simulating the biological vision system has not been widely used in deep learning dehazing algorithms. To alleviate the difficulty of feature interaction, we combine the feedback mechanism with dense skip connections to fuse features of different levels in a dehazing network. Inspired by the feedback network in which previous network layers can have access to rich information processed by the following network layers, we propose an end-to-end dense feedback network (DFBDehazeNet) for single image dehazing that implements the feedback mechanism using hidden states of constrained RNN. The low-level hazy feature information can be continuously corrected by the high-level feature information obtained from the dense feedback block via the recurrent feedback connection. The top-down feedback mechanism is adopted in DFBDehazeNet to refine the low-level hazy feature information, thereby achieving a powerful image restoration effect. The ablation experiment proves that the iterative structure of DFBDehazeNet and the projection unit play an important role in removing haze from images. The experimental results show that the results of image haze removal are superior to the great majority of existing methods both qualitatively and quantitatively.

MSAR‐DefogNet: Lightweight cloud removal network for high resolution remote sensing images based on multi scale convolution

High resolution remote sensing image cloud removal can bring a lot of convenience for human activities. However, the existing cloud removal algorithms have a variety of disadvantages. First of all, they have the disadvantages of long computing time and large consumption of computing resources. Secondly, the effect of recovery needs to be improved. In order to improve the above two points, a near real-time effective algorithm is proposed, namely MSAR-Defognet (multiple scale attention residual network using for cloud remove), which consumes less computing power and space and has superior cloud removal effect. On the one hand, several different large-scale filters are chosen to extract the weak information effectively, while can save the computing power and shorten the image processing time. On the other hand, the fine-grained convolution residual block with channel attention mechanism is used to enhance the network's ability to extract cloud features. In addition, a data set which is closer to the real cloud shape and has higher richness to train the cloud removal network, so that the parameters obtained by training have stronger robustness and can adaptively remove clouds with different thickness. Experiments show that, compared with other advanced network models, the network not only has the advantage of fast processing speed, but also has better image restoration effect in high-resolution remote sensing image restoration. It can meet the requirements of many hard real-time tasks, so that remote sensing images can play a greater value for human activities.

Image restoration model using Jaya‐Bat optimization‐enabled noise prediction map

Image restoration approaches are introduced to restore the latent clear images from the degraded images. However, the performance of the existing approaches remains an open problem, which may leads to the further development of advanced image restoration techniques. Therefore, an effective image restoration method is developed for restoring the input image from various noises, like impulse noise and random noise. The generation of pixel map, identification of noisy pixel, and the enhancement of pixel are the three major phases involved in the proposed method. Initially, the noisy pixel map generation is performed from the input image, and then the noisy pixels are identified based on deep convolutional neural network, which is trained by the proposed Jaya-Bat algorithm. The Jaya-Bat algorithm is developed by combing the Jaya optimization algorithm and Bat algorithm. Once the noisy pixels are identified, the pixel enhancement is done using the neuro fuzzy system. The experiment is carried out using Statlog (landsat satellite) dataset, and the developed method achieves the maximal peak signal to noise ratio of 51.03 dB, maximal structural similarity index of 0.848 for the image with random noise, and the maximal second derivative like measure of enhancement 62.96 dB with impulse noise, respectively.