Restoration Of Blurred Images Research Articles

Meta-learning based blind image super-resolution approach to different degradations

Although recent studies on blind single image super-resolution (SISR) have achieved significant success, most of them typically require supervised training on synthetic low resolution (LR)-high resolution (HR) paired images. This leads to re-training necessity for different degradations and restricted applications in real-world scenarios with unfavorable inputs. In this paper, we propose an unsupervised blind SISR method with input underlying different degradations, named different degradations blind super-resolution (DDSR). It formulates a Gaussian modeling on blur degradation and employs a meta-learning framework for solving different image degradations. Specifically, a neural network-based kernel generator is optimized by learning from random kernel samples, referred to as random kernel learning. This operation provides effective initialization for blur degradation optimization. At the same time, a meta-learning framework is proposed to resolve multiple degradation modelings on the basis of alternative optimization between blur degradation and image restoration, respectively. Differing from the pre-trained deep-learning methods, the proposed DDSR is implemented in a plug-and-play manner, and is capable of restoring HR image from unfavorable LR input with degradations such as partial coverage, noise addition, and darkening. Extensive simulations illustrate the superior performance of the proposed DDSR approach compared to the state-of-the-arts on public datasets with comparable memory load and time consumption, yet exhibiting better application flexibility and convenience, and significantly better generalization ability towards multiple degradations. Our code is available at https://github.com/XYLGroup/DDSR.

Image Deblurring Based on Efficient Transformer and Multi-scale CNN

The study of image deblurring techniques in dynamic scenes is a high-profile research direction. Recently, given the excellence of Convolutional Neural Networks (CNNs) in extracting feature information, it has become a common and effective practice to utilize them for blurred image restoration work. However, CNN can only model local information and has a limited receptive field, which inhibits the deblurring effect. Transformer can model global information, so it can be combined with CNN to expand the receptive field and enhance the deblurring effect. Unfortunately, as the spatial resolution of the input image increases, the computational complexity of the Transformer increases dramatically, showing a trend of square-level growth, which makes it difficult to cope with the task of processing high-resolution images. To address the above problems, this paper proposes an image deblurring network based on efficient Transformer and multi-scale CNN called ET-MIMO-UNet. The local spatial features are extracted using multi-scale CNN and embedded into the global characteristics of the Transformer, modelling both local and global information. To solve the problem of difficult training due to large image size and to improve the computational efficiency, an efficient Transformer layer (ETL) is designed, which contains a multi-dconv head transposed attention (MHTA) and a gated-dconv feed-forward network(GFFN). In addition, a multi-layer feature fusion block (MFFB) is introduced to fuse full-scale features and reduce feature loss. On the GoPro test dataset, compared with the MIMO-UNet base network, the PSNR of the three models of ET-MIMO-UNet is improved by 0.39dB, 0.54dB, and 0.66dB, respectively; ET-MIMO-UNet reduces the number of parameters by half compared with MPRNet. The experimental data fully proved that the method demonstrated a significant processing effect in coping with the image-blurring problem in dynamic scenes.

Bidirectional image denoising with blurred image feature

Image denoising remains a classical yet still challenging problem, because the noise can destroy details and cause severe information loss. In recent years, various well-designed CNN-based methods have been extensively applied in image denoising because of the strong learning ability. However, most of them share an unidirectional procedure, which directly learns a mapping from noisy input to a clean image without focusing on the over-smoothed state of the denoising process, limiting the richness of extracted features. Different from previous works, we propose a blurred image feature guided CNN (BFCNN) network that implements a novel blurring-adjusting strategy to address the complex denoising problem via two stages. In stage 1, we build a blurring module (BM) to capture over-smoothed features from noisy observations and generate the blurred image restoration, which is a less informative version of the clean image. Furthermore, a multi-level concatenating module (CM) and an adjusting module (AM) are then designed to recover more detailed information in stage 2. These two modules are jointly designed to restore a properly-smoothed image from the over-smoothed blurred image and the given under-smoothed noisy image. Comparing to the traditional denoising process, the proposed blurring-adjusting strategy produces a precise denoised image more efficiently by converting the unidirectional denoising process into a bidirectional denoising process. To our knowledge, this is the first study that utilizes the over-smoothed image to address the denoising problem. Extensive experiments demonstrate the superiority of our BFCNN with more accurate reconstruction quality and achieve competitive quantitative results among current CNN-based methods. This research will release the code upon acceptance.

Restoration and enhancement optimization of blurred images based on SRGAN

Blurred images pose a significant challenge in many applications, including medical imaging, remote sensing, and surveillance systems. These images suffer from low resolution, noise, and missing data, which can hinder their interpretation and analysis. Traditional methods for image restoration and enhancement have their limitations, such as low quality and slow processing times. To overcome these challenges, this paper proposes an innovative method using Super-resolution Generative Adversarial Networks (SRGANs) to enhance image quality and fidelity. The proposed method employs adversarial training, perceptual loss, residual learning, and feature reconstruction to generate visually realistic and high-quality super-resolution (SR) images from low-resolution (LR) inputs. The SRGANs approach outperforms traditional methods, demonstrating its potential to advance image restoration and enhancement techniques. The paper also discusses possible improvements and future directions for this technique.

Blind motion deblurring using improved DeblurGAN

AbstractTo develop a fast and effective image deblurring method, the blind recovery of motion‐blurred images based on DeblurGAN(GAN, Generative Adversarial Networks) is researched.Firstly, the number of residual modules in the DeblurGAN network is changed, and an attempt is made to optimize the network structure to achieve better results in the blind recovery of motion‐blurred images. Secondly, an image deblurring method based on the improved DeblurGAN network is proposed.This paper makes corresponding adjustments to the generator and discriminator networks to change their input and output size to 512 × 512 while keeping the overall structure of the network unchanged, and the experimental results show that the quality of the restored images has been greatly improved. In addition, the images were divided into four classes of images,to achieve improved restoration of blurred images with specific content. Experimental results on the benchmark GoPro dataset validate that the improved DeblurGAN achieves more than 1.5 dB improvement than DeblurGAN in terms of peak signal‐to‐noise ratio (PSNR) as trained utilizing the same amount of data, and structural similarity assessment (SSIM) evaluation means and maximum values increased between 0.2 and 0.3. The improved DeblurGAN is more effective in terms of both blur removal and detail recovery.

Restoration of a blurred photographic image of a moving object obtained at the resolution limit

Objectives. When processing images of the Earth’s surface obtained from satellites, the problem of restoring a blurry image of a moving object is of great practical importance. The aim of this work is to study the possibility of improving the quality of restoration of blurry images obtained at the limit of the resolution of the camera.Methods. Digital signal processing methods informed by the theory of incorrect and ill-conditioned problems were used.Results. The proposed method for restoring a blurred photographic image of a moving object differs from traditional approaches in that the discrete convolution equation, to which the problem of restoring a blurred image is reduced, is obtained by approximating the corresponding integral equation based on the Kotelnikov interpolation series rather than on the traditional basis of the quadrature formula. In the work, formulas are obtained for calculating the kernel of the convolution obtained using the Kotelnikov interpolation series. The discrete convolution inversion problem, which belongs to the class of ill-posed problems, requires regularization. Results of traditional approaches to restoring blurred images using the quadrature formula with Tikhonov regularization and the proposed method based on the Kotelnikov interpolation series are compared. Although the quality of the blurred image restoration is almost the same in both cases, in the quadrature formula the blur value is expressed as an integer number of pixels, while, when using the Kotelnikov series, this value can also be specified in fractions of a pixel.Conclusions. The expediency of discretizing the convolution describing the image distortion of the blur type on the basis of the Kotelnikov interpolation series when processing a blurred image obtained at the limit of the resolution of the camera is demonstrated. In this case, the amount of blur can be expressed in fractions of a pixel. This situation typically arises when processing satellite photography of the Earth’s surface.

A blurred star image restoration method based on gyroscope data and enhanced sparse model

Abstract Star sensors usually have a fixed exposure time to guarantee detection of adequate navigation stars. In a high dynamic environment, star images suffer from degradation due to spacecraft movement, which will severely affects both centroid extraction and attitude accuracy. This paper presents an algorithm for the restoration of motion-blurred star images. The algorithm employs gyroscope assistance and consists of two steps: preprocessing and motion-blurred image restoration. In the preprocessing step, the angular velocity of the gyroscope predicts the motion trajectory, position, and shape of each star point during exposure. This step ensures a good initial estimate of the blur kernel for image restoration. The image restoration step employs an enhanced sparse model inspired by blind deblurring method to solve blur kernel and latent image alternately. Simulations and experiments have verified the effectiveness of the proposed algorithm.

Research on Digital Meter Reading Method of Inspection Robot Based on Deep Learning

Aiming at solving the issue of blurred images and difficult recognition of digital meters encountered by inspection robots in the inspection process, this paper proposes a deep-learning-based method for blurred image restoration and LED digital identification. Firstly, fast Fourier transform (FFT) is used to perform blur detection on the acquired images. Then, the blurred images are recovered using spatial-attention-improved adversarial neural networks. Finally, the digital meter region is extracted using the polygon-YOLOv5 model and corrected via perspective transformation. The digits in the image are extracted using the YOLOv5s model, and then recognized by the CRNN for digit recognition. It is experimentally verified that the improved adversarial neural network in this paper achieves 26.562 in the PSNR metric and 0.861 in the SSIM metric. The missing rate of the digital meter reading method proposed in the paper is only 1% and the accuracy rate is 98%. The method proposed in this paper effectively overcomes the image blurring problem caused by the detection robot during the detection process. This method solves the problems of inaccurate positioning and low digital recognition accuracy of LED digital meters in complex and changeable environments, and provides a new method for reading digital meters.

Prior information-based motion blur image restoration method for the low-frequency sinusoidal vibration measurements with machine vision

The sinusoidal vibration is the basic unit of motion control and measurement, which can constitute the arbitrary plane and space motions and is essential to the low-frequency vibration calibration. At present, the machine vision (MV) method has been widely applied to measure the low-frequency vibration, which can promote the improvement of low-frequency vibration calibration and metrology traceability. However, the MV method is always inevitably affected by the motion blur which decreases its measurement accuracy. In this study, a novel prior information-based motion blur image restoration method is investigated, which applies the known sinusoidal information and the Lucy-Richardson (LR) method with Non-Local (NL)-Means to eliminate the motion blur and improve the measurement accuracy. Meanwhile, the objective evaluation indexes and edge detection method are adopted to verify the restoration effect. Several simulations and actual experiment show that the investigated method can significantly improve the quality of the sinusoidal motion blur images, and further make the accuracies of the edge detection method and MV method increase 0.3 pixel and 0.3%, respectively.

SharpFormer: Learning Local Feature Preserving Global Representations for Image Deblurring

The goal of dynamic scene deblurring is to remove the motion blur presented in a given image. To recover the details from the severe blurs, conventional convolutional neural networks (CNNs) based methods typically increase the number of convolution layers, kernel-size, or different scale images to enlarge the receptive field. However, these methods neglect the non-uniform nature of blurs, and cannot extract varied local and global information. Unlike the CNNs-based methods, we propose a Transformer-based model for image deblurring, named SharpFormer, that directly learns long-range dependencies via a novel Transformer module to overcome large blur variations. Transformer is good at learning global information but is poor at capturing local information. To overcome this issue, we design a novel Locality preserving Transformer (LTransformer) block to integrate sufficient local information into global features. In addition, to effectively apply LTransformer to the medium-resolution features, a hybrid block is introduced to capture intermediate mixed features. Furthermore, we use a dynamic convolution (DyConv) block, which aggregates multiple parallel convolution kernels to handle the non-uniform blur of inputs. We leverage a powerful two-stage attentive framework composed of the above blocks to learn the global, hybrid, and local features effectively. Extensive experiments on the GoPro and REDS datasets show that the proposed SharpFormer performs favourably against the state-of-the-art methods in blurred image restoration.

Severe motion blurred silkworm pupae image restoration in sex discrimination

Severe motion blurred silkworm pupae image restoration in sex discrimination

RMOBF-Net: Network for the Restoration of Motion and Optical Blurred Finger-Vein Images for Improving Recognition Accuracy

Biometrics is a method of recognizing a person based on one or more unique physical and behavioral characteristics. Since each person has a different structure and shape, it is highly secure and more convenient than the existing security system. Among various biometric authentication methods, finger-vein recognition has advantages in that it is difficult to forge because a finger-vein exists inside one’s finger and high user convenience because it uses a non-invasive device. However, motion and optical blur may occur for some reasons such as finger movement and camera defocusing during finger-vein recognition, and such blurring occurrences may increase finger-vein recognition error. However, there has been no research on finger-vein recognition considering both motion and optical blur. Therefore, in this study, we propose a new method for increasing finger-vein recognition accuracy based on a network for the restoration of motion and optical blurring in a finger-vein image (RMOBF-Net). Our proposed network continuously maintains features that can be utilized during motion and optical blur restoration by actively using residual blocks and feature concatenation. Also, the architecture RMOBF-Net is optimized to the finger-vein image domain. Experimental results are based on two open datasets, the Shandong University homologous multi-modal traits finger-vein database and the Hong Kong Polytechnic University finger-image database version 1, from which equal error rates of finger-vein recognition accuracy of 4.290–5.779% and 2.465–6.663% were obtained, respectively. Higher performance was obtained from the proposed method compared with that of state-of-the-art methods.

Investigation on the blurred image restoration based on brain‐inspired model

Though relatively good effect has been achieved by the image de-blurring method based on deep learning, the existing methods still suffer from the problem of unclear restoration of the edges. Therefore, brain-inspired image restoration model based on human attention and “fine vision” is proposed to improve the blind restoration quality of the image in this paper according to the response mechanism of the different cerebral cortices for high and low spatial resolutions. The designed brain-inspired model consists of dual-channel network available to realize the function of feature merger for low and high resolutions, which is used to extract the image edges with detailed information filtered out. Confirmatory experiment is implemented based on the blurred image in the data set of GOPRO, LIVE and set14. As per the result, the model proposed is available for relatively good restoration of blurred image and super-resolution, as well as looking results by visual inspection.

A modified Dai–Kou-type method with applications to signal reconstruction and blurred image restoration

A modified Dai–Kou-type method with applications to signal reconstruction and blurred image restoration

Image Restoration using Nonlocal Regularized Variational Model with Spatially Adapted Regularization Parameter

Restoration of blurred images corrupted with impulse noise has attracted increasing attention due to its great potential toward practical applications. To guarantee the noisy image deblurring performance, total variation (TV), one of the most popular regularizers, has been extensively exploited in the literature. This local regularizer is known for its capability of preserving edges and penalizing oscillations but not sharp discontinuities. However, TV regularized variational models commonly suffer from undesirable staircase-like artifacts in homogeneous regions. The reason behind this phenomenon is that TV regularizer favors solutions that are piecewise constant in numerical experiments. To overcome the model-dependent deficiency, we proposed to replace the commonly used local regularizer by its nonlocal extension, i.e., nonlocal total variation (NLTV) regularizer. By taking advantage of the high degree of self-similarity within images, the proposed nonlocal variational model is able to remove both blurring and impulse noise while preserving meaningful image details. To guarantee solution stability and efficiency, the resulting nonsmooth image restoration problem was effectively dealt with using an alternating optimization algorithm. In addition, to further enhance the image quality, a local variance estimator-based automatic method was introduced to calculate the spatially adapted regularization parameters during image restoration. Extensive experiments have demonstrated that the proposed method could achieve superior imaging performance compared with several state-of-the-art image restoration methods.

An optimization method for motion blur image restoration and ringing suppression via texture mapping

Since the image sensor will produce blur problems in the process of collecting data of moving objects, the image needs to be restored. Ringing is one of the most common artifacts in deblurred images. This paper proposes a non-blind image deconvolution method based on texture mapping segmentation, named texture-Richardson–Lucy (TRL) algorithm, which suppresses ringing while deblurring the image. TRL is based on a novel ringing removal deconvolution algorithm, which adds a ringing detection term as regularization in the iterative process of the Richardson–Lucy algorithm. Taking into account the structural difference between the texture and the flat area, the image is segmented into several blocks and restored through adaptive iterative texture maps based on the pixel intensity and texture features of the image. In order to obtain a reasonable texture map, a Gaussian mixture model is used to fit the pixel intensity distribution, and use the expectation maximization algorithm and local binary mode to estimate. Experimental results and quantitative evaluations show that TRL can effectively reduce ringing artifacts while retaining details and achieving robustness to suppress ringing of different blur kernels. The processing time of a single 1 million pixel image in an 8-core CPU environment is about 3.5 s. And the PSNR and SSIM parameters are above 30 dB and 0.92, respectively. In conclusion, TRL is superior to the current popular algorithms.

A Survey on Face and Body Based Human Recognition Robust to Image Blurring and Low Illumination

Many studies have been actively conducted on human recognition in indoor and outdoor environments. This is because human recognition methods in such environments are closely related to everyday life situations. Besides, these methods can be applied for finding missing children and identifying criminals. Methods for human recognition in indoor and outdoor environments can be classified into three categories: face-, body-, and gait-based methods. There are various factors that hinder indoor and outdoor human recognition, for example, blurring of captured images, cutoff in images due to the camera angle, and poor recognition in images acquired in low-illumination environments. Previous studies conducted to solve these problems focused on facial recognition only. This is because the face is typically assumed to contain more important information for human recognition than the body. However, when a human face captured by a distant camera is small, or even impossible to identify with the naked eye, the body’s information can help with recognition. For this reason, this survey paper reviews both face- and body-based human recognition methods. In previous surveys, recognition on low-resolution images were reviewed. However, survey papers on blurred images are not comprehensive. Therefore, in this paper, we review studies on blurred image restoration in detail by classifying them based on whether deep learning was used and whether the human face and body were combined. Although previous survey papers on recognition covered low-illumination environments as well, they excluded deep learning methods. Therefore, in this survey, we also include details on deep-learning-based low-illumination image recognition methods. We aim to help researchers who will study related fields in the future.

BANet: A Blur-Aware Attention Network for Dynamic Scene Deblurring.

Image motion blur results from a combination of object motions and camera shakes, and such blurring effect is generally directional and non-uniform. Previous research attempted to solve non-uniform blurs using self-recurrent multi-scale, multi-patch, or multi-temporal architectures with self-attention to obtain decent results. However, using self-recurrent frameworks typically leads to a longer inference time, while inter-pixel or inter-channel self-attention may cause excessive memory usage. This paper proposes a Blur-aware Attention Network (BANet), that accomplishes accurate and efficient deblurring via a single forward pass. Our BANet utilizes region-based self-attention with multi-kernel strip pooling to disentangle blur patterns of different magnitudes and orientations and cascaded parallel dilated convolution to aggregate multi-scale content features. Extensive experimental results on the GoPro and RealBlur benchmarks demonstrate that the proposed BANet performs favorably against the state-of-the-arts in blurred image restoration and can provide deblurred results in real-time.

Blurred Image Restoration Conditional Generative Adversarial Network Based on Attention Mechanism

Blurred Image Restoration Conditional Generative Adversarial Network Based on Attention Mechanism

An adaptive two phase blind image deconvolution algorithm for an iterative regularization model

This paper proposes a blind image deconvolution method which consists of two sequential phases, i.e., blur kernel estimation and image restoration. In the first phase, we adopt the L0-norm of image gradients and total variation (TV) to regularize the latent image and blur kernel, respectively. Then we design an alternating optimization algorithm which jointly incorporates the estimation of intermediately restored image, blur kernel and regularization parameters into account. In the second phase, we propose to take the mixture of L0-norm of image gradients and TV to regularize the latent image, and design an efficient non-blind deconvolution algorithm to achieve the restored image. Experimental results on both a benchmark image dataset and real-world blurred images show that the proposed method can effectively restore image details while suppress noise and ringing artifacts, the result is of high quality which is competitive with some state of the art methods.