Blind Multi-image Super-resolution Reconstruction with Motion Blur Estimation

2013 ◽  
Vol 12 (19) ◽  
pp. 4875-4881
Author(s):  
Fengqing Qin
Keyword(s):  
Super Resolution ◽  
Motion Blur ◽  
Image Super Resolution ◽  
Blur Estimation

Video super resolution using convolutional neural network and image fusion techniques

2021 ◽  
Vol 24 (4) ◽  
pp. 279-287
Author(s):  
Vikas Kumar ◽  
Tanupriya Choudhury ◽  
Suresh Chandra Satapathy ◽  
Ravi Tomar ◽  
Archit Aggarwal
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
Reference Model ◽  
Super Resolution ◽  
Motion Blur ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Recently, huge progress has been achieved in the field of single image super resolution which augments the resolution of images. The idea behind super resolution is to convert low-resolution images into high-resolution images. SRCNN (Single Resolution Convolutional Neural Network) was a huge improvement over the existing methods of single-image super resolution. However, video super-resolution, despite being an active field of research, is yet to benefit from deep learning. Using still images and videos downloaded from various sources, we explore the possibility of using SRCNN along with image fusion techniques (minima, maxima, average, PCA, DWT) to improve over existing video super resolution methods. Video Super-Resolution has inherent difficulties such as unexpected motion, blur and noise. We propose Video Super Resolution – Image Fusion (VSR-IF) architecture which utilizes information from multiple frames to produce a single high- resolution frame for a video. We use SRCNN as a reference model to obtain high resolution adjacent frames and use a concatenation layer to group those frames into a single frame. Since, our method is data-driven and requires only minimal initial training, it is faster than other video super resolution methods. After testing our program, we find that our technique shows a significant improvement over SCRNN and other single image and frame super resolution techniques.

Blind Single-Image Super-Resolution Reconstruction Based on Motion Blur

2011 ◽  
Vol 225-226 ◽  
pp. 895-899 ◽  
Author(s):  
Feng Qing Qin ◽  
Zhong Li ◽  
Li Hong Zhu ◽  
Ying De You ◽  
Li Lan Cao
Keyword(s):  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Difficult Problem ◽  
Motion Blur ◽  
Parameter Analysis ◽  
Single Image ◽  
Imaging Model ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Error Parameter

Blind image super-resolution reconstruction is one of the hot and difficult problem in image processing. a framework of blind single-image super-resolution reconstruction algorithm is proposed. In the low-resolution imaging model, the processes of motion blur, down-sampling, and noise are considered. The parameter of motion blur is estimated through an error-parameter analysis method. Utilizing Wiener filtering image restoration algorithm, an error-parameter curve at different motion distance is generated, from which the motion distance of the motion point spread function (PSF) can be estimated approximately. The super-resolution image is reconstructed through the iterative back projection (IBP) algorithm. The experimental results show that motion PSF estimation plays an important role on the quality of the SR reconstructed image, and also demonstrate the effectiveness of the proposed algorithm.

Multi-task Learning-based All-in-one Collaboration Framework for Degraded Image Super-resolution

2021 ◽  
Vol 17 (1) ◽  
pp. 1-21
Author(s):  
Xin Jin ◽  
Jianfeng Xu ◽  
Kazuyuki Tasaka ◽  
Zhibo Chen
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Input Image ◽  
Motion Blur ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Task Learning ◽  
Degraded Image ◽  
Image Super Resolution ◽  
Low Resolution Images

In this article, we address the degraded image super-resolution problem in a multi-task learning (MTL) manner. To better share representations between multiple tasks, we propose an all-in-one collaboration framework (ACF) with a learnable “junction” unit to handle two major problems that exist in MTL—“How to share” and “How much to share.” Specifically, ACF consists of a sharing phase and a reconstruction phase. Considering the intrinsic characteristic of multiple image degradations, we propose to first deal with the compression artifact, motion blur, and spatial structure information of the input image in parallel under a three-branch architecture in the sharing phase. Subsequently, in the reconstruction phase, we up-sample the previous features for high-resolution image reconstruction with a channel-wise and spatial attention mechanism. To coordinate two phases, we introduce a learnable “junction” unit with a dual-voting mechanism to selectively filter or preserve shared feature representations that come from sharing phase, learning an optimal combination for the following reconstruction phase. Finally, a curriculum learning-based training scheme is further proposed to improve the convergence of the whole framework. Extensive experimental results on synthetic and real-world low-resolution images show that the proposed all-in-one collaboration framework not only produces favorable high-resolution results while removing serious degradation, but also has high computational efficiency, outperforming state-of-the-art methods. We also have applied ACF to some image-quality sensitive practical task, such as pose estimation, to improve estimation accuracy of low-resolution images.

A Short Survey of Image Super Resolution Algorithms

2015 ◽  
Vol 2 (2) ◽  
pp. 19-29 ◽  
Author(s):  
Heng Liu ◽  
◽  
Dongdong Huang
Keyword(s):  
Super Resolution ◽  
Short Survey ◽  
Image Super Resolution

Facial Image Super-Resolution Reconstruction Based on Separated Frequency Components

2013 ◽  
Vol E96.A (6) ◽  
pp. 1315-1322 ◽  
Author(s):  
Hyunduk KIM ◽  
Sang-Heon LEE ◽  
Myoung-Kyu SOHN ◽  
Dong-Ju KIM ◽  
Byungmin KIM
Keyword(s):  
Super Resolution ◽  
Facial Image ◽  
Frequency Components ◽  
Image Super Resolution

A Novel Approach for Image Super Resolution Problems with Convolution Neural Networks

2017 ◽  
Vol 6 (4) ◽  
pp. 15
Author(s):  
JANARDHAN CHIDADALA ◽  
RAMANAIAH K.V. ◽  
BABULU K ◽  
◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Super Resolution ◽  
Convolution Neural Networks ◽  
Novel Approach ◽  
Image Super Resolution

A Fast Decoupling Algorithm for Image Super-resolution Reconstruction of Space-invariant System

2010 ◽  
Vol 36 (2) ◽  
pp. 229-236 ◽  
Author(s):  
Li-Li HUANG ◽  
Liang XIAO ◽  
Zhi-Hui WEI ◽  
Jun ZHANG
Keyword(s):  
Super Resolution ◽  
Invariant System ◽  
Image Super Resolution ◽  
Decoupling Algorithm

Deep fractal residual network for fast and accurate single image super resolution

2020 ◽  
Vol 398 ◽  
pp. 389-398 ◽  
Author(s):  
Yanghao Zhou ◽  
Jianfeng Dong ◽  
Yubin Yang
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Residual Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution
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