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

Author(s):  
Amanjot Singh ◽  
Jagroop Singh

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.

Author(s):  
Qiang Yu ◽  
Feiqiang Liu ◽  
Long Xiao ◽  
Zitao Liu ◽  
Xiaomin Yang

Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden layers in convolutional neural networks (CNN) help the model learn useful information. However, there exists redundancy among feature maps, which can be further exploited. To address these issues, this paper proposes a lightweight efficient feature generating network (EFGN) for SISR by constructing the efficient feature generating block (EFGB). Specifically, the EFGB can conduct plain operations on the original features to produce more feature maps with parameters slightly increasing. With the help of these extra feature maps, the network can extract more useful information from low resolution (LR) images to reconstruct the desired high resolution (HR) images. Experiments conducted on the benchmark datasets demonstrate that the proposed EFGN can outperform other deep-learning based methods in most cases and possess relatively lower model complexity. Additionally, the running time measurement indicates the feasibility of real-time monitoring.


Author(s):  
Vishal Chudasama ◽  
Kishor Upla ◽  
Kiran Raja ◽  
Raghavendra Ramachandra ◽  
Christoph Busch

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Kai Shao ◽  
Qinglan Fan ◽  
Yunfeng Zhang ◽  
Fangxun Bao ◽  
Caiming Zhang

2021 ◽  
Vol 213 ◽  
pp. 106663
Author(s):  
Yujie Dun ◽  
Zongyang Da ◽  
Shuai Yang ◽  
Yao Xue ◽  
Xueming Qian

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