scholarly journals Image Restoration Based on Adaptive Dual-Domain Filtering

2018 ◽  
Vol 2018 ◽  
pp. 1-17
Author(s):  
Ruiqiang He ◽  
Xiangchu Feng ◽  
Chenping Zhao ◽  
Huazhu Chen ◽  
Xiaolong Zhu ◽  
...  
Keyword(s):  
Image Restoration ◽  
Learning Algorithm ◽  
Super Resolution ◽  
Image Deblurring ◽  
Superior Performance ◽  
Single Image ◽  
Denoising Method ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Dual Domain

Image restoration is a long-standing problem in low-level computer vision. In this paper, we offer a simple but effective estimation paradigm for various image restoration problems. Specifically, we first propose a model-based Gaussian denoising method Adaptive Dual-Domain Filtering (ADDF) by learning the optimal confidence factors which are adjusted adaptively with Gaussian noise standard deviation. In addition, by generalizing this learning approach to Laplace noise, the learning algorithm of the optimum confidence factors in Laplace denoising is presented. Finally, the proposed ADDF is tactfully plugged into the method frameworks of off-the-shelf image deblurring and single image super-resolution (SISR). The approach, coining the name Plug-ADDF, achieves promising performance. Extensive experiments validate that the proposed ADDF for Gaussian and Laplace noise removals indeed results in visual and quantitative improvements over some existing state-of-the-art methods. Moreover, our Plug-ADDF for image deblurring and SISR also demonstrates superior performance objectively and subjectively.

Generating High-Quality Air-Coupled Ultrasonic Images for Wooden Material Characterization by Single Image Super-Resolution

2019 ◽  
Vol 34 (03) ◽  
pp. 2054008
Author(s):  
Lujun Lin ◽  
Yiming Fang ◽  
Xiaochen Du ◽  
Zhu Zhou
Keyword(s):  
Image Quality ◽  
Super Resolution ◽  
Visual Assessment ◽  
Wood Products ◽  
Superior Performance ◽  
Single Image ◽  
High Quality ◽  
Practical Applications ◽  
Image Super Resolution ◽  
Single Image Super Resolution

As the practical applications in other fields, high-resolution images are usually expected to provide a more accurate assessment for the air-coupled ultrasonic (ACU) characterization of wooden materials. This paper investigated the feasibility of applying single image super-resolution (SISR) methods to recover high-quality ACU images from the raw observations that were constructed directly by the on-the-shelf ACU scanners. Four state-of-the-art SISR methods were applied to the low-resolution ACU images of wood products. The reconstructed images were evaluated by visual assessment and objective image quality metrics, including peak signal-to-noise-ratio and structural similarity. Both qualitative and quantitative evaluations indicated that the substantial improvement of image quality can be yielded. The results of the experiments demonstrated the superior performance and high reproducibility of the method for generating high-quality ACU images. Sparse coding based super-resolution and super-resolution convolutional neural network (SRCNN) significantly outperformed other algorithms. SRCNN has the potential to act as an effective tool to generate higher resolution ACU images due to its flexibility.

scholarly journals DEEP RESIDUAL LEARNING FOR SINGLE-IMAGE SUPER-RESOLUTION OF MULTI-SPECTRAL SATELLITE IMAGERY

2019 ◽  
Vol IV-2/W7 ◽  
pp. 189-196
Author(s):  
L. Wagner ◽  
L. Liebel ◽  
M. Körner
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Network Architecture ◽  
Super Resolution ◽  
Superior Performance ◽  
Optical Remote Sensing ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
The Individual

<p><strong>Abstract.</strong> Analyzing optical remote sensing imagery depends heavily on their spatial resolution. At the same time, this data is adversely affected by fixed sensor parameters and environmental influences. Methods for increasing the quality of such data and concomitantly optimizing its information content are, thus, in high demand. In particular, single-image super-resolution (SISR) approaches aim to achieve this goal solely by observing the individual images.</p><p>We propose to adapt a generic deep residual neural network architecture for SISR to deal with the special properties of remote sensing satellite imagery, especially taking into account the different spatial resolutions of individual Sentinel-2 bands, i.e., ground sampling distances of 20&amp;thinsp;m and 10&amp;thinsp;m. As a result, this method is able to increase the perceived resolution of the 20&amp;thinsp;m channels and mesh all spectral bands. Experimental evaluation and ablation studies on large datasets have shown superior performance compared to the state-of-the-art and that the model is not bound by its capacity.</p>

scholarly journals Deep Residual Dense Network for Single Image Super-Resolution

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 555
Author(s):  
Yogendra Rao Musunuri ◽  
Oh-Seol Kwon
Keyword(s):  
Super Resolution ◽  
Superior Performance ◽  
Perceptual Quality ◽  
Dense Network ◽  
Single Image ◽  
Proposed Model ◽  
Image Super Resolution ◽  
Perceptual Characteristics ◽  
Single Image Super Resolution ◽  
The Stability

In this paper, we propose a deep residual dense network (DRDN) for single image super- resolution. Based on human perceptual characteristics, the residual in residual dense block strategy (RRDB) is exploited to implement various depths in network architectures. The proposed model exhibits a simple sequential structure comprising residual and dense blocks with skip connections. It improves the stability and computational complexity of the network, as well as the perceptual quality. We adopt a perceptual metric to learn and assess the quality of the reconstructed images. The proposed model is trained with the Diverse2k dataset, and the performance is evaluated using standard datasets. The experimental results confirm that the proposed model exhibits superior performance, with better reconstruction results and perceptual quality than conventional methods.

scholarly journals Multi-Branch Deep Residual Network for Single Image Super-Resolution

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 144 ◽  
Author(s):  
Peng Liu ◽  
Ying Hong ◽  
Yan Liu
Keyword(s):  
Super Resolution ◽  
Superior Performance ◽  
Evaluation Function ◽  
Single Image ◽  
Residual Network ◽  
Perceptual Evaluation ◽  
Benchmark Datasets ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Recently, algorithms based on the deep neural networks and residual networks have been applied for super-resolution and exhibited excellent performance. In this paper, a multi-branch deep residual network for single image super-resolution (MRSR) is proposed. In the network, we adopt a multi-branch network framework and further optimize the structure of residual network. By using residual blocks and filters reasonably, the model size is greatly expanded while the stable training is also guaranteed. Besides, a perceptual evaluation function, which contains three parts of loss, is proposed. The experiment results show that the evaluation function provides great support for the quality of reconstruction and the competitive performance. The proposed method mainly uses three steps of feature extraction, mapping, and reconstruction to complete the super-resolution reconstruction and shows superior performance than other state-of-the-art super-resolution methods on benchmark datasets.

scholarly journals Cross-Dimension Attention Guided Self-Supervised Remote Sensing Single-Image Super-Resolution

2021 ◽  
Vol 13 (19) ◽  
pp. 3835
Author(s):  
Wenzong Jiang ◽  
Lifei Zhao ◽  
Yanjiang Wang ◽  
Weifeng Liu ◽  
Baodi Liu
Keyword(s):  
Remote Sensing ◽  
Super Resolution ◽  
Remote Sensing Image ◽  
Training Data ◽  
Superior Performance ◽  
Single Image ◽  
Remote Sensing Images ◽  
Image Super Resolution ◽  
The Cross ◽  
Single Image Super Resolution

In recent years, the application of deep learning has achieved a huge leap in the performance of remote sensing image super-resolution (SR). However, most of the existing SR methods employ bicubic downsampling of high-resolution (HR) images to obtain low-resolution (LR) images and use the obtained LR and HR images as training pairs. This supervised method that uses ideal kernel (bicubic) downsampled images to train the network will significantly degrade performance when used in realistic LR remote sensing images, usually resulting in blurry images. The main reason is that the degradation process of real remote sensing images is more complicated. The training data cannot reflect the SR problem of real remote sensing images. Inspired by the self-supervised methods, this paper proposes a cross-dimension attention guided self-supervised remote sensing single-image super-resolution method (CASSISR). It does not require pre-training on a dataset, only utilizes the internal information reproducibility of a single image, and uses the lower-resolution image downsampled from the input image to train the cross-dimension attention network (CDAN). The cross-dimension attention module (CDAM) selectively captures more useful internal duplicate information by modeling the interdependence of channel and spatial features and jointly learning their weights. The proposed CASSISR adapts well to real remote sensing image SR tasks. A large number of experiments show that CASSISR has achieved superior performance to current state-of-the-art methods.

scholarly journals Single-Image Super-Resolution of Sentinel-2 Low Resolution Bands with Residual Dense Convolutional Neural Networks

2021 ◽  
Vol 13 (24) ◽  
pp. 5007
Author(s):  
Luis Salgueiro ◽  
Javier Marcello ◽  
Verónica Vilaplana
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Superior Performance ◽  
Single Image ◽  
Low Resolution ◽  
Spatial Coverage ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Sentinel 2

Sentinel-2 satellites have become one of the main resources for Earth observation images because they are free of charge, have a great spatial coverage and high temporal revisit. Sentinel-2 senses the same location providing different spatial resolutions as well as generating a multi-spectral image with 13 bands of 10, 20, and 60 m/pixel. In this work, we propose a single-image super-resolution model based on convolutional neural networks that enhances the low-resolution bands (20 m and 60 m) to reach the maximal resolution sensed (10 m) at the same time, whereas other approaches provide two independent models for each group of LR bands. Our proposed model, named Sen2-RDSR, is made up of Residual in Residual blocks that produce two final outputs at maximal resolution, one for 20 m/pixel bands and the other for 60 m/pixel bands. The training is done in two stages, first focusing on 20 m bands and then on the 60 m bands. Experimental results using six quality metrics (RMSE, SRE, SAM, PSNR, SSIM, ERGAS) show that our model has superior performance compared to other state-of-the-art approaches, and it is very effective and suitable as a preliminary step for land and coastal applications, as studies involving pixel-based classification for Land-Use-Land-Cover or the generation of vegetation indices.

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