NDLI: DeepInterpolation: fusion of multiple interpolations and CNN to obtain super-resolution

Content Provider	IET Digital Library
Author	Pandey, Ram Krishna Ganesan, Ramakrishnan Angarai
Abstract	The authors propose architectures that learn end-to-end mapping functions to improve the spatial resolution of the input natural images. The models are unique in forming non-linear combinations of three image interpolation techniques using the convolutional neural network. Another proposed architecture uses a skip connection with nearest-neighbour interpolation, achieving almost similar results. The architectures have been carefully designed to ensure that the reconstructed images lie precisely in the manifold of high-resolution images, thereby preserving the high-frequency components with fine details. They have compared with the state-of-the-art and recent deep learning-based natural image super-resolution techniques and found that their methods can preserve the sharp details in the image, while also obtaining comparable or better peak-signal-to-noise ratio values than them. Since their methods use image interpolations and a shallow convolutional neural network (CNN) with a fewer number of smaller filters, the computational cost is kept low. They have reported the results of the best two proposed architectures on five standard data sets for an upscale factor of 2. Their methods generalise well in most cases, which is evident from the better results obtained with increasingly complex data sets. For four times upscaling, they have designed similar architectures for comparing with other methods.
Starting Page	4000
Ending Page	4011
Page Count	12
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 15, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1244
Journal	IET Image Processing
Publisher Date	2020-12-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	CNN Computer Vision And Image Processing Technique Convolutional Neural Nets End-to-end Mapping Function Filtering Method in Signal Processing High-frequency Component High-resolution Image Image Filtering Image Fusion Image Interpolation Technique Image Interpolations Image Reconstruction Image Resolution Input Natural Image Interpolation Interpolation And Function Approximation Learning in AI Multiple Interpolations Nearest-Neighbour Interpolation Nonlinear Combinations Numerical Analysis Optical, Image And Video Signal Processing Peak-signal-to-noise Ratio Value Recent Deep Learning-based Natural Image Super-resolution Technique Reconstructed Image Shallow Convolutional Neural Network Similar Architectures Skip Connection Spatial Resolution
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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