Image Super-resolution Reconstruction Method Research Articles

Generative Adversarial Network-Based Edge-Preserving Superresolution Reconstruction of Infrared Images

The convolutional neural network has achieved good results in the superresolution reconstruction of single-frame images. However, due to the shortcomings of infrared images such as lack of details, poor contrast, and blurred edges, superresolution reconstruction of infrared images that preserves the edge structure and better visual quality is still challenging. Aiming at the problems of low resolution and unclear edges of infrared images, this work proposes a two-stage generative adversarial network model to reconstruct realistic superresolution images from four times downsampled infrared images. In the first stage of the generative adversarial network, it focuses on recovering the overall contour information of the image to obtain clear image edges; the second stage of the generative adversarial network focuses on recovering the detailed feature information of the image and has a stronger ability to express details. The infrared image superresolution reconstruction method proposed in this work has highly realistic visual effects and good objective quality evaluation results.

Spatial Scale Effect of a Typical Polarized Remote Sensor on Detecting Ground Objects.

For polarized remote sensors, the polarization images of ground objects acquired at different spatial scales will be different due to the spatial heterogeneity of the ground object targets and the limitation of imaging resolution. In this paper, the quantitative inversion problem of a typical polarized remote sensor at different spatial scales was studied. Firstly, the surface roughness of coatings was inversed based on the polarized bidirectional reflectance distribution function (pBRDF) model according to their polarization images at different distances. A linear-mixed pixel model was used to make a preliminary correction of the spatial scale effect. Secondly, the super-resolution image reconstruction of the polarization imager was realized based on the projection onto convex sets (POCS) method. Then, images with different resolutions at a fixed distance were obtained by utilizing this super-resolution image reconstruction method and the optimal spatial scale under the scene can be acquired by using information entropy as an evaluation indicator. Finally, the experimental results showed that the roughness inversion of coatings has the highest accuracy in the optimal spatial scale. It has been proved that our proposed method can provide a reliable way to reduce the spatial effect of the polarized remote sensor and to improve the inversion accuracy.

An image enhancement algorithm of video surveillance scene based on deep learning

Target enhancement is the most important task in a video surveillance system. In order to improve the accuracy and efficiency of target enhancement, and better deal with the subsequent recognition, tracking, behaviour understanding and other processing of targets, a deep learning-based image enhancement algorithm for video surveillance scenes is proposed. First, the super-resolution reconstruction of the image is carried out through the image super-resolution reconstruction method based on the hybrid deep convolutional network to improve the sharpness of the image. Then, for the reconstructed video surveillance scene image, the watershed image enhancement algorithm based on morphology and region merging is used to realize the enhancement of the video surveillance scene image. Deep learning algorithms can improve the accuracy of image enhancement through iterative calculations. Experimental results show that after image enhancement in daytime, night and noisy video surveillance scenes, the maximum enhancement difference rate is less than 0.5%, the cross-linking degree is close to 1, and the average image enhancement time is less than 1.3 s. It can realize image enhancement of video surveillance scenes and improve the image clarity of the video surveillance scene.

Logo Based on Improved Generative Countermeasure Network Image Super Resolution Reconstruction Method

In order to solve the problem that the existing learning based super-resolution reconstruction methods rely too much on the degenerate model while the degradation model is unknown, which leads to noise and edge jagged in the reconstructed image, an image super-resolution reconstruction method based on improved generative countermeasure network is proposed. The generator of the network consists of up sampling module, denoising module and anti-aliasing module Firstly, the input image is sampled by the up sampling module to generate the initial super-resolution image; then, the denoising module and anti-aliasing module are used to reconstruct the clear super-resolution image; in order to reconstruct a better logo image, a joint training loss is introduced, including content loss and resistance loss, and the content loss includes perception loss and edge loss Lost. The experimental results show that, compared with the current super-resolution reconstruction method based on generative countermeasure network (SRGAN), the peak signal-to-noise ratio (PSNR) of the reconstructed image is improved to 0.2675dB and the structural similarity is improved to 0.035, which can effectively improve the quality of logo image reconstruction.

Online Phase Measurement Profilometry for a Fast-Moving Object

When the measured object is fast moving online, the captured deformed pattern may appear as motion blur, and some phase information will be lost. Therefore, the frame rate has to be improved by adjusting the image acquisition mode of the camera to adapt to a fast-moving object, but the resolution of the captured deformed pattern will be sacrificed. So a super-resolution image reconstruction method based on maximum a posteriori (MAP) estimation is adopted to obtain high-resolution deformed patterns, and in this way, the reconstructed high-resolution deformed patterns also have a good effect on noise suppression. Finally, all the reconstructed high-resolution equivalent phase shifting deformed patterns are used for online three-dimensional (3D) reconstruction. Experimental results prove the effectiveness of the proposed method. The proposed method has a good application prospect in high-precision and fast online 3D measurement.

SAR Parametric Super-Resolution Image Reconstruction Methods Based on ADMM and Deep Neural Network

The compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods have emerged as the standard approach to obtain super-resolution (SR) SAR images and achieve extraordinary performances. However, they face three challenges. First, this kind of method is mainly based on the point scattering model and not suitable for characterizing the line-segment-scattering and surface-scattering features of distributed targets. Second, the hyperparameters in these methods are hard to tune to optimal values. Third, due to a large amount of calculation, these methods are difficult to apply in practice. In this article, to solve these problems, we introduce the line-segment-scatterers (LSSs) and rectangular-plate-scatterers (RPSs) in SAR echo model to develop the SAR hybrid echo model and propose two SAR parametric SR image reconstruction methods based on solving a CS problem, where three penalties are utilized to exploit the sparsity of the point scatterers, LSSs, and RPSs, respectively. At the core of the first method is a direct solver called multicomponent alternating direction method of multipliers (MC-ADMM) solver that solves the CS problem quickly and iteratively based on closed derivative expressions. In contrast, the second method maps the MC-ADMM solver into a deep unfolded neural network, i.e., the parametric SR imaging network (PSRI-Net), which is faster, and the parameters can be automatically set to the optimum. Since all the parameters of the MC-ADMM solver are learned discriminatively through end-to-end training in PSRI-Net. Extensive simulation and practical experiments are carried out to demonstrate the effectiveness of the proposed methods.

X-ray image super-resolution reconstruction based on a multiple distillation feedback network

The super-resolution reconstruction of X-ray images is one of the hot issues in the field of medical imaging. Due to the limitations of X-ray machines, the acquired images often have some problems, such as blurred details, unclear edges and low contrast, which seriously affect doctors’ interpretations of X-ray images. In view of the above problems, an X-ray image super-resolution reconstruction method based on a multiple distillation feedback network is proposed. In the feature extraction stage, the shallow features of X-ray images are extracted through 3 × 3 and 1 × 1 convolutional layers, and a multiple distillation feedback module is designed, which iteratively upsamples and downsamples to fully extract the texture details of X-ray images. Subpixel convolution is used to improve the resolution and the residual convolution is used to predict the corresponding residual image. In the image reconstruction stage, the residual image is fused with the transposed convolution upsampled image, and the Laplacian pyramid structure is used to progressively reconstruct high-resolution X-ray images. The experimental results show that the proposed method can suppress noise and reduce artifacts. Its peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and information fidelity criteria (IFC) were all higher than those of the comparison methods, and its subjective visual was better.

Image super-resolution reconstruction based on feature map attention mechanism

To improve the issue of low-frequency and high-frequency components from feature maps being treated equally in existing image super-resolution reconstruction methods, the paper proposed an image super-resolution reconstruction method using attention mechanism with feature map to facilitate reconstruction from original low-resolution images to multi-scale super-resolution images. The proposed model consists of a feature extraction block, an information extraction block, and a reconstruction module. Firstly, the extraction block is used to extract useful features from low-resolution images, with multiple information extraction blocks being combined with the feature map attention mechanism and passed between feature channels. Secondly, the interdependence is used to adaptively adjust the channel characteristics to restore more details. Finally, the reconstruction module reforms different scales high-resolution images. The experimental results can demonstrate that the proposed method can effectively improve not only the visual effect of images but also the results on the Set5, Set14, Urban100, and Manga109. The results can demonstrate the proposed method has structurally similarity to the image reconstruction methods. Furthermore, the evaluating indicator of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) has been improved to a certain degree, while the effectiveness of using feature map attention mechanism in image super-resolution reconstruction applications is useful and effective.

Image super-resolution reconstruction for secure data transmission in Internet of Things environment.

The image super-resolution reconstruction method can improve the image quality in the Internet of Things (IoT). It improves the data transmission efficiency, and is of great significance to data transmission encryption. Aiming at the problem of low image quality in image super-resolution using neural networks, a self-attention-based image reconstruction method is proposed for secure data transmission in IoT environment. The network model is improved, and the residual network structure and sub-pixel convolution are used to extract the feature of the image. The self-attention module is used extract detailed information in the image. Using generative confrontation method and image feature perception method to improve the image reconstruction effect. The experimental results on the public data set show that the improved network model improves the quality of the reconstructed image and can effectively restore the details of the image.

Image Super-Resolution Algorithm Based on RRDB Model

Aiming at the problems of texture distortion and fuzzy details in the existing image super-resolution reconstruction methods, a super-resolution reconstruction network based on multi-channel attention mechanism is proposed. The texture extraction module designs an extremely lightweight multi-channel attention module in the network structure. Combined with one-dimensional convolution, it realizes cross-channel information interaction, focusing on important feature information; The texture restoration module introduces dense remaining blocks to restore some high-frequency texture details, improve the model performance, and generate high-quality reconstructed images. The proposed network can not only effectively improve the visual effect of the image, but the results on the benchmark data set CUFED5 are similar to the classic super-resolution (SRCNN) reconstruction method based on convolutional neural network. The peak signal-to-noise ratio (PSNR) and structure are similar. Degrees (SSIM) increased by 1.76dB and 0.062 respectively. Experimental results show that the designed network can improve the accuracy of texture migration and can effectively improve the quality of the generated image.

基于自注意力深度网络的图像超分辨率重建方法

基于自注意力深度网络的图像超分辨率重建方法

An Image Super-Resolution Reconstruction Method with Single Frame Character Based on Wavelet Neural Network in Internet of Things

The application of the traditional single frame character image super-resolution reconstruction method has some problems, such as noise can not be removed completely and anti-interference performance is poor. A new method for the super-resolution reconstruction of single frame character image based on wavelet neural network is proposed. The structure and interface of image acquisition unit of solid state image sensor are designed. Combined with pinhole imaging model and camera self-calibration, image acquisition of Internet of Things is completed. An image degradation model was established to simulate the degradation process of ideal high-resolution image to low-resolution image. Wavelet threshold denoising method is used to remove the noise in a single frame character image and improve the anti-interference performance of the method. The wavelet neural network reflection model is used to reconstruct the single frame feature image and improve the resolution of the image. The experimental results show that the blur degree of the reconstructed image is always less than 5%. In the whole experiment, the accuracy of this method can be maintained at 80% ~ 90%. The image detail retention rate of the research method is relatively stable. With the increase of the number of experimental images, the retention rate of image details remains between 80% and 95%, indicating that the method is effective in practical application.

Multi-scale fractal residual network for image super-resolution

Recent studies have shown that the use of deep convolutional neural networks (CNNs) can improve the performance of single image super-resolution reconstruction (SISR) methods. However, the existing CNN-based SISR model ignores the multi-scale features and shallow and deep features of the image, resulting in relatively low image reconstruction performance. To address these issues, this paper proposes a new multi-scale fractal residual network (MSFRN) for image super-resolution. On the basis of residual learning, a multi-scale fractal residual block (MSFRB) is designed. This block uses convolution kernels of different sizes to extract image multi-scale features and uses multiple paths to extract and fuse image features of different depths. Then, the shallow features extracted at the shallow feature extraction stage and the local features output by all MSFRBs are used to perform global hierarchical feature fusion. Finally, through sub-pixel convolution, the fused global features are used to reconstruct high-resolution images from low-resolution images. The experimental results on the five standard benchmark datasets show that MSFRN improved subjective visual effects and objective image quality evaluation indicators, and is superior to other state-of-the-art SISR methods.

Super‐resolution image reconstruction using molecular docking

Molecular-docking is an essential tool in the drug designing process, where a small molecule ligand (drug) binds with disease-causing protein molecule to prevent its further activity. Docking process helps in predicting the most appropriate configuration and the optimal interaction energy between the interacting molecules (ligand and protein) to form a stable complex. Based on this idea, a new learning-based single image super-resolution reconstruction (LSI-SRR) method is proposed here. Estimation of a high resolution (HR) patch is achieved by optimising the interaction energy between the input low resolution patch and its corresponding candidate patches appropriately chosen from the training image dataset via Genetic algorithm. Structured-spatiogram based measure; a new and competent similarity criterion is proposed to select potentially efficient training images which encompass better statistical and structural co-relation with the input image. The proposed method is tested on synthetic and real-time images at different magnification factors. Performance analysis of the proposed work is compared with some of the representative state-of-the-art LSI-SRR methods. Experimental results demonstrate that the proposed method produces HR images with enhanced image details, minimal artefacts and most importantly enables an efficient trade-off between the image qualities to speed than the competing methods.

Brief Survey of Single Image Super-Resolution Reconstruction Based on Deep Learning Approaches

With the presentation of super-resolution convolutional neural network, deep learning approach was applied to image super-resolution reconstruction for the first time. By using convolutional neural network, the deep learning approaches can directly learn the mapping relationship between the low-resolution image and high-resolution image, and have achieved better reconstruction effects than the traditional image super-resolution reconstruction methods. Subsequently, a series of improved deep learning approaches have been proposed, and the reconstruction effects have been improved continuously. This paper systematically summa rizes the image super-resolution reconstruction approaches based on deep learning, analyzes the characteristics of different models, and compares the main deep learning models based on the experiments. Furthermore, based on deep learning model, the future research directions of the image super-resolution reconstruction methods based on deep learning models are reasonably predicted.

Product appearance detection based on visual keywords matching

The product appearance detection device based on the USB (Universal Serial Bus) camera is studied. The USB camera is used as the front device of the image acquisition and the host computer is a PC for the detection system. Aiming at low resolution of USB camera, the super-resolution image reconstruction method based on sparse representation is used to overcome the low resolution for the low-cost imaging sensor and improve the image resolution. A method of product appearance detection based on visual keywords is proposed. Visual keywords are used to describe characteristics of defects. Visual keyword extraction is performed on sample images and test images, and visual keywords are used for matching. Identify defective products by setting different thresholds. Experiments show that the proposed system has better detection results and offers good robustness to the angle and illumination of the image from the product appearance. In this paper, the theory of visual keywords is applied to product appearance detection for the first time, which provides a reference for further research.

Research on Product Appearance Detection System Based on Image Sparse Representation

The USB camera is used as the front device of the image acquisition. The host computer is a PC for the detection system. Aiming at low resolution of USB camera, the super-resolution image reconstruction method based on sparse representation is used to overcome the low resolution for the low-cost imaging sensor and improve the image resolution. In order to reduce the influence due to shooting light, angle, etc., visual keywords are used for image matching to improve the robustness of the system. The experiment proves that the system is simple in structure and easy to operate, and the detection accuracy of the system can reach 98.67%.

Face Super-Resolution Reconstruction Based on Self-Attention Residual Network

Aiming at the problems of the face image super-resolution reconstruction method based on convolutional neural network, such as single feature extraction scale, low utilization rate of features and blurred face images texture, a model combining convolutional neural network with self-attention mechanism is proposed. Firstly, the shallow features of the image are extracted by the cascaded 3 × 3 convolutional kernels, and then self-attention mechanism is combined with the residual blocks in depth residual network to extract the deep detail features of faces. Finally, the extracted features are fused globally by skip connections, which provide more high-frequency details for face reconstruction. Experiments on Helen, CelebA face datasets and real-world images showed that the proposed method could make full use of facial feature information, and its peak signal to noise ratio (PSNR) and structural similarity (SSIM) were both higher than the comparison methods with better subjective visual effects.

Super-Resolution Reconstruction of Single Image based on Multiscale Recursive Residual Network

In order to solve the problems of single image super-resolution algorithm based on convolutional neural network, such as shallow network structure, single feature extraction scale and fuzzy texture of reconstructed image, a single image super-resolution reconstruction method based on multi-scale convolutional neural network is proposed. The convolution of several different scales is used to check the original low-resolution image for feature detail extraction, and the recurrent residual network is used to gradually restore the high-frequency information of the image with fewer parameters. Finally, the final reconstructed image is obtained by fusion of the features extracted by different convolutional kernels. The experimental results show that the algorithm proposed in this paper has obtained better image super-resolution result, can obtain more detailed information, and get better visual effect. The classical reconstruction methods such as bicubic, SRCNN, FSRCNN, ESPCN, VDSR are compared, and the peak signal-to-noise ratio (PSNR) and the structure similarity (SSIM) are higher than that of the existing algorithms.

Image super-resolution reconstruction based on multi-scale feature loss function

In the image super-resolution reconstruction, many methods based on deep learning mostly adopt the traditional mean squared error (MSE) as the loss function, and the reconstructed image is prone to the problem of fuzzy details and too smooth. In order to solve this problem, this paper improves the traditional mean square error loss function and proposes an image super-resolution reconstruction method based on multi-scale feature loss function. The whole network model consists of a DenseNet-based reconstruction model and a convolutional neural network which is used to optimize the multi-scale feature loss function. Taking the reconstructed image and the corresponding original HD image as the input of the convolved neural network in series, the mean square error of the different scale feature images obtained by convolution of the reconstructed image with the corresponding original HD image was calculated. Experimental results show that the method in this paper is improved in subjective vision, PSRN and SSIM.