Gaussian Noised Single-Image Super Resolution Reconstruction

2013 ◽  
Vol 457-458 ◽  
pp. 1032-1036
Author(s):  
Feng Qing Qin ◽  
Li Hong Zhu ◽  
Li Lan Cao ◽  
Wa Nan Yang
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Projection Algorithm ◽  
Single Image ◽  
Low Resolution ◽  
Filtering Algorithm ◽  
Resolution Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution

A framework is proposed to reconstruct a super resolution image from a single low resolution image with Gaussian noise. The degrading processes of Gaussian blur, down-sampling, and Gaussian noise are all considered. For the low resolution image, the Gaussian noise is reduced through Wiener filtering algorithm. For the de-noised low resolution image, iterative back projection algorithm is used to reconstruct a super resolution image. Experiments show that de-noising plays an important part in single-image super resolution reconstruction. In the super reconstructed image, the Gaussian noise is reduced effectively and the peak signal to noise ratio (PSNR) is increased.

Single-Image Super Resolution Reconstruction with Pepper and Salt Noise

2013 ◽  
Vol 427-429 ◽  
pp. 1817-1821
Author(s):  
Feng Qing Qin ◽  
Li Hong Zhu ◽  
Li Lan Cao ◽  
Wa Nan Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Projection Algorithm ◽  
Single Image ◽  
Low Resolution ◽  
Resolution Image ◽  
Imaging Model ◽  
Resolution Imaging ◽  
Image Super Resolution ◽  
Single Image Super Resolution

In order to improve the resolution of single image with Pepper and Salt noise, a framework is proposed. In the low resolution imaging model, the Gaussian blur, down-sampling, as well as Pepper and Salt noise are considered. For the low resolution image, the Pepper and Salt noise is reduced through median filtering method. Super resolution reconstruction is performed on the de-noised low resolution image by iterative back projection algorithm. Experimental results show that the Pepper and Salt noise are removed effectively and the peak signal to noise ratio (PSNR) of the super resolution reconstructed image is improved.

scholarly journals Single Image Super Resolution Technique: An Extension to True Color Images

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 464
Author(s):  
Muhammad Irfan ◽  
Sahib Khan ◽  
Arslan Arif ◽  
Khalil Khan ◽  
Aleem Khaliq ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Noise Removal ◽  
Reconstruction Error ◽  
Single Image ◽  
Low Resolution ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Low Resolution Images

The super-resolution (SR) technique reconstructs a high-resolution image from single or multiple low-resolution images. SR has gained much attention over the past decade, as it has significant applications in our daily life. This paper provides a new technique of a single image super-resolution on true colored images. The key idea is to obtain the super-resolved image from observed low-resolution images. A proposed technique is based on both the wavelet and spatial domain-based algorithms by exploiting the advantages of both of the algorithms. A back projection with an iterative method is implemented to minimize the reconstruction error and for noise removal wavelet-based de-noising method is used. Previously, this technique has been followed for the grayscale images. In this proposed algorithm, the colored images are taken into account for super-resolution. The results of the proposed method have been examined both subjectively by observation of the results visually and objectively by considering the peak signal-to-noise ratio (PSNR) and mean squared error (MSE), which gives significant results and visually better in quality from the bi-cubic interpolation technique.

scholarly journals Multi-scale Xception based depthwise separable convolution for single image super-resolution

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0249278
Author(s):  
Wazir Muhammad ◽  
Supavadee Aramvith ◽  
Takao Onoye
Keyword(s):  
Research Work ◽  
Super Resolution ◽  
Single Image ◽  
Low Resolution ◽  
Significant Drop ◽  
Great Ability ◽  
Resolution Image ◽  
Significant Performance ◽  
Image Super Resolution ◽  
Single Image Super Resolution

The main target of Single image super-resolution is to recover high-quality or high-resolution image from degraded version of low-quality or low-resolution image. Recently, deep learning-based approaches have achieved significant performance in image super-resolution tasks. However, existing approaches related with image super-resolution fail to use the features information of low-resolution images as well as do not recover the hierarchical features for the final reconstruction purpose. In this research work, we have proposed a new architecture inspired by ResNet and Xception networks, which enable a significant drop in the number of network parameters and improve the processing speed to obtain the SR results. We are compared our proposed algorithm with existing state-of-the-art algorithms and confirmed the great ability to construct HR images with fine, rich, and sharp texture details as well as edges. The experimental results validate that our proposed approach has robust performance compared to other popular techniques related to accuracy, speed, and visual quality.

scholarly journals Single Image Super-Resolution viaL0Image Smoothing

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhang Liu ◽  
Qi Huang ◽  
Jian Li ◽  
Qi Wang
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Smoothing Method ◽  
Single Image ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image ◽  
Sharp Edges ◽  
Image Super Resolution ◽  
Single Image Super Resolution

We propose a single image super-resolution method based on aL0smoothing approach. We consider a low-resolution image as two parts: one is the smooth image generated by theL0smoothing method and the other is the error image between the low-resolution image and the smoothing image. We get an intermediate high-resolution image via a classical interpolation and then generate a high-resolution smoothing image with sharp edges by theL0smoothing method. For the error image, a learning-based super-resolution approach, keeping image details well, is employed to obtain a high-resolution error image. The resulting high-resolution image is the sum of the high-resolution smoothing image and the high-resolution error image. Experimental results show the effectiveness of the proposed method.

Image Super-Resolution via Low-Rank Representation

2014 ◽  
Vol 568-570 ◽  
pp. 652-655 ◽  
Author(s):  
Zhao Li ◽  
Le Wang ◽  
Tao Yu ◽  
Bing Liang Hu
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Low Rank ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image ◽  
High Resolution Images ◽  
Low Rank Representation ◽  
Image Super Resolution ◽  
Single Image Super Resolution

This paper presents a novel method for solving single-image super-resolution problems, based upon low-rank representation (LRR). Given a set of a low-resolution image patches, LRR seeks the lowest-rank representation among all the candidates that represent all patches as the linear combination of the patches in a low-resolution dictionary. By jointly training two dictionaries for the low-resolution and high-resolution images, we can enforce the similarity of LLRs between the low-resolution and high-resolution image pair with respect to their own dictionaries. Therefore, the LRR of a low-resolution image can be applied with the high-resolution dictionary to generate a high-resolution image. Unlike the well-known sparse representation, which computes the sparsest representation of each image patch individually, LRR aims at finding the lowest-rank representation of a collection of patches jointly. LRR better captures the global structure of image. Experiments show that our method gives good results both visually and quantitatively.

scholarly journals A Sparse Analysis-Based Single Image Super-Resolution

Computers ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 41 ◽  
Author(s):  
Vahid Anari ◽  
Farbod Razzazi ◽  
Rasoul Amirfattahi
Keyword(s):  
Computational Complexity ◽  
High Resolution ◽  
Similarity Index ◽  
Super Resolution ◽  
Structural Similarity ◽  
Image Texture ◽  
Single Image ◽  
Low Resolution ◽  
Image Super Resolution ◽  
Single Image Super Resolution

In the current study, we were inspired by sparse analysis signal representation theory to propose a novel single-image super-resolution method termed “sparse analysis-based super resolution” (SASR). This study presents and demonstrates mapping between low and high resolution images using a coupled sparse analysis operator learning method to reconstruct high resolution (HR) images. We further show that the proposed method selects more informative high and low resolution (LR) learning patches based on image texture complexity to train high and low resolution operators more efficiently. The coupled high and low resolution operators are used for high resolution image reconstruction at a low computational complexity cost. The experimental results for quantitative criteria peak signal to noise ratio (PSNR), root mean square error (RMSE), structural similarity index (SSIM) and elapsed time, human observation as a qualitative measure, and computational complexity verify the improvements offered by the proposed SASR algorithm.

scholarly journals Deep Image Prior for Super Resolution of Noisy Image

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2014
Author(s):  
Sujy Han ◽  
Tae Bok Lee ◽  
Yong Seok Heo
Keyword(s):  
Estimation Method ◽  
Super Resolution ◽  
Optimization Process ◽  
Single Image ◽  
Target Image ◽  
Resolution Image ◽  
Image Prior ◽  
Deep Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Single image super-resolution task aims to reconstruct a high-resolution image from a low-resolution image. Recently, it has been shown that by using deep image prior (DIP), a single neural network is sufficient to capture low-level image statistics using only a single image without data-driven training such that it can be used for various image restoration problems. However, super-resolution tasks are difficult to perform with DIP when the target image is noisy. The super-resolved image becomes noisy because the reconstruction loss of DIP does not consider the noise in the target image. Furthermore, when the target image contains noise, the optimization process of DIP becomes unstable and sensitive to noise. In this paper, we propose a noise-robust and stable framework based on DIP. To this end, we propose a noise-estimation method using the generative adversarial network (GAN) and self-supervision loss (SSL). We show that a generator of DIP can learn the distribution of noise in the target image with the proposed framework. Moreover, we argue that the optimization process of DIP is stabilized when the proposed self-supervision loss is incorporated. The experiments show that the proposed method quantitatively and qualitatively outperforms existing single image super-resolution methods for noisy images.

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