scholarly journals Image Super Resolution Reconstruction Based on Improved Invertible Rescaling Net

2021 ◽  
Author(s):  
Taiping Mo ◽  
Dehong Chen
Keyword(s):  
Feature Extraction ◽  
High Frequency ◽  
Interpolation Method ◽  
Super Resolution ◽  
Frequency Information ◽  
Ill Posed ◽  
Image Edge ◽  
Image Super Resolution ◽  
Improved Model ◽  
Image Details

Abstract The Invertible Rescaling Net (IRN) is modeling image downscaling and upscaling as a unified task to alleviate the ill-posed problem in the super-resolution task. However, the ability of potential variables of the model embedded high-frequency information is general, which affects the performance of the reconstructed image. In order to improve the ability of embedding high-frequency information and further reduce the complexity of the model, the potential variables and feature extraction of key components of IRN are improved. Attention mechanism and dilated convolution are used to improve the feature extraction block, reduce the parameters of feature extraction block, and allocate more attention to the image details. The high frequency sub-band interpolation method of wavelet domain is used to improve the potential variables, process and save the image edge, and enhance the ability of embedding high frequency information. Experimental results show that compared with IRN model, improved model has less complexity and excellent performance.

scholarly journals Gradient-Guided Convolutional Neural Network for MRI Image Super-Resolution

2019 ◽  
Vol 9 (22) ◽  
pp. 4874 ◽  
Author(s):  
Xiaofeng Du ◽  
Yifan He
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Frequency ◽  
Super Resolution ◽  
Resonance Imaging ◽  
Reconstruction Accuracy ◽  
Main Challenge ◽  
Image Super Resolution ◽  
Magnetic Resonance Imaging Mri ◽  
Image Details

Super-resolution (SR) technology is essential for improving image quality in magnetic resonance imaging (MRI). The main challenge of MRI SR is to reconstruct high-frequency (HR) details from a low-resolution (LR) image. To address this challenge, we develop a gradient-guided convolutional neural network for improving the reconstruction accuracy of high-frequency image details from the LR image. A gradient prior is fully explored to supply the information of high-frequency details during the super-resolution process, thereby leading to a more accurate reconstructed image. Experimental results of image super-resolution on public MRI databases demonstrate that the gradient-guided convolutional neural network achieves better performance over the published state-of-art approaches.

scholarly journals Infrared Image Super Resolution by Combining Compressive Sensing and Deep Learning

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2587 ◽  
Author(s):  
Xudong Zhang ◽  
Chunlai Li ◽  
Qingpeng Meng ◽  
Shijie Liu ◽  
Yue Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Compressive Sensing ◽  
High Frequency ◽  
Infrared Imaging ◽  
Infrared Image ◽  
Super Resolution ◽  
Infrared Images ◽  
Frequency Information ◽  
Image Super Resolution

Super resolution methods alleviate the high cost and high difficulty in applying high resolution infrared image sensors. In this paper we present a novel single image super resolution method for infrared images by combining compressive sensing theory and deep learning. Low resolution images can be regarded as the compressed sampling results of the high resolution ones in compressive sensing. With sparsity in this theory, higher resolution images can be reconstructed. However, because of diverse level of sparsity for different images, the output contains noise and loss of high frequency information. Deep convolutional neural network provides a solution to relieve the noise and supplement some missing high frequency information. By concatenating two methods, we manage to produce better results in super resolution tasks for infrared images than SRCNN and ScSR. PSNR and SSIM values are used to quantify the performance. Applying our method to open datasets and actual infrared imaging experiments, we also find better visual results are preserved.

Research on Improvement Algorithm of Image Edge Detection

2014 ◽  
Vol 539 ◽  
pp. 141-145
Author(s):  
Shui Li Zhang
Keyword(s):  
Cognitive Psychology ◽  
Edge Detection ◽  
High Frequency ◽  
Detection Method ◽  
Low Frequency ◽  
Frequency Information ◽  
Image Edge Detection ◽  
Improvement Algorithm ◽  
Image Edge ◽  
Edge Detection Method

This paper presents new theorems Stevens edge detection method based on cognitive psychology on. Firstly, based on the number of the image is decomposed into high-frequency and low-frequency information, and the high-frequency information extracted by subtracting the maximum number of images to the image after the filter, then the amount of high frequency information into psychological cognitive psychology based on Stevenss theorem. The algorithm suppression refined edge after the non-minimum, applications Pillar K-means algorithm to extract image edge. Experimental results show that: the brightness of the image is converted to the amount of psychological edge can better unify under different brightness values.

scholarly journals Depth image super-resolution reconstruction based on a modified joint trilateral filter

2019 ◽  
Vol 6 (1) ◽  
pp. 181074 ◽  
Author(s):  
Dongsheng Zhou ◽  
Ruyi Wang ◽  
Xin Yang ◽  
Qiang Zhang ◽  
Xiaopeng Wei
Keyword(s):  
Super Resolution ◽  
Structural Similarity ◽  
Depth Image ◽  
High Quality ◽  
Depth Images ◽  
Trilateral Filter ◽  
Coding Method ◽  
Image Edge ◽  
Image Super Resolution ◽  
Sr Reconstruction

Depth image super-resolution (SR) is a technique that uses signal processing technology to enhance the resolution of a low-resolution (LR) depth image. Generally, external database or high-resolution (HR) images are needed to acquire prior information for SR reconstruction. To overcome the limitations, a depth image SR method without reference to any external images is proposed. In this paper, a high-quality edge map is first constructed using a sparse coding method, which uses a dictionary learned from the original images at different scales. Then, the high-quality edge map is used to guide the interpolation for depth images by a modified joint trilateral filter. During the interpolation, some information of gradient and structural similarity (SSIM) are added to preserve the detailed information and suppress the noise. The proposed method can not only preserve the sharpness of image edge, but also avoid the dependence on database. Experimental results show that the proposed method is superior to some state-of-the-art depth image SR methods.

scholarly journals Content adaptive single image interpolation based super resolution of compressed images

2020 ◽  
Vol 10 (3) ◽  
pp. 3014
Author(s):  
Amanjot Singh ◽  
Jagroop Singh
Keyword(s):  
Interpolation Method ◽  
Super Resolution ◽  
Original Image ◽  
Single Image ◽  
Standard Methods ◽  
Compressed Images ◽  
Adaptive Interpolation ◽  
Image Super Resolution ◽  
Simulation Results ◽  
Content Adaptive

Image Super resolution is used to upscale the low resolution Images. It is also known as image upscaling .This paper focuses on upscaling of compressed images based on Interpolation techniques. A content adaptive interpolation method of image upscaling has been proposed. This interpolation based scheme is useful for single image based Super-resolution (SR) methods .The presented method works on horizontal, vertical and diagonal directions of an image separately and it is adaptive to the local content of an image. This method relies only on single image and uses the content of the original image only; therefore the proposed method is more practical and realistic. The simulation results have been compared to other standard methods with the help of various performance matrices like PSNR, MSE, MSSIM etc. which indicates the preeminence of the proposed method.

scholarly journals SUPER RESOLUTION RECONSTRUCTION BASED ON ADAPTIVE DETAIL ENHANCEMENT FOR ZY-3 SATELLITE IMAGES

2016 ◽  
Vol III-7 ◽  
pp. 213-217 ◽  
Author(s):  
Hong Zhu ◽  
Weidong Song ◽  
Hai Tan ◽  
Jingxue Wang ◽  
Di Jia
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Satellite Images ◽  
Interpolation Method ◽  
Super Resolution ◽  
Bilateral Filter ◽  
Redundant Information ◽  
Evaluation Standard ◽  
Detail Enhancement ◽  
Image Details

Super-resolution reconstruction of sequence remote sensing image is a technology which handles multiple low-resolution satellite remote sensing images with complementary information and obtains one or more high resolution images. The cores of the technology are high precision matching between images and high detail information extraction and fusion. In this paper puts forward a new image super resolution model frame which can adaptive multi-scale enhance the details of reconstructed image. First, the sequence images were decomposed into a detail layer containing the detail information and a smooth layer containing the large scale edge information by bilateral filter. Then, a texture detail enhancement function was constructed to promote the magnitude of the medium and small details. Next, the non-redundant information of the super reconstruction was obtained by differential processing of the detail layer, and the initial super resolution construction result was achieved by interpolating fusion of non-redundant information and the smooth layer. At last, the final reconstruction image was acquired by executing a local optimization model on the initial constructed image. Experiments on ZY-3 satellite images of same phase and different phase show that the proposed method can both improve the information entropy and the image details evaluation standard comparing with the interpolation method, traditional TV algorithm and MAP algorithm, which indicate that our method can obviously highlight image details and contains more ground texture information. A large number of experiment results reveal that the proposed method is robust and universal for different kinds of ZY-3 satellite images.

An Effective Regularization Method for Image Super Resolution

2011 ◽  
Vol 219-220 ◽  
pp. 1411-1414
Author(s):  
En Wei Zheng ◽  
Xian Jun Wang
Keyword(s):  
Super Resolution ◽  
License Plate ◽  
Reconstruction Method ◽  
Ill Posed ◽  
High Resolution Images ◽  
Image Super Resolution ◽  
Sr Reconstruction ◽  
Real Traffic ◽  
Reconstruction Model

In this paper, we propose a new super resolution (SR) reconstruction method to handle license plate numbers of vehicles in real traffic videos. Recently, SR reconstruction shemes based on regularization have been demonstrated to be effective because SR reconstrction is an ill-posed problem. Working within this promising framework, the residual data (RD) term can be weighted according to the differences among the observed LR images in the SR reconstruction model. Moreover, L1 norm is used to measure the RD term in order to improve the robustness of our method. Experiments show the proposed method improves the subjective visual quality of the high resolution images.

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