Super resolution imaging using modified lanr based on separable filtering

Recently, remarkable advances have been achieved in reconstructing high-resolution image from noisy, and low-resolution images. Reaching super resolution has been a challenge in image processing practices, because of their under-constrained nature that requires the missing HR image details to be...

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Main Author:	Somadina, Ike Chidiebere
Format:	Thesis
Language:	English
Published:	2019
Subjects:	High resolution imaging Image processing - Digital techniques Algorithms
Online Access:	http://psasir.upm.edu.my/id/eprint/77392/1/FK%202019%201%20ir.pdf
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spelling	my-upm-ir.773922022-01-28T02:16:07Z Super resolution imaging using modified lanr based on separable filtering 2019-04 Somadina, Ike Chidiebere Recently, remarkable advances have been achieved in reconstructing high-resolution image from noisy, and low-resolution images. Reaching super resolution has been a challenge in image processing practices, because of their under-constrained nature that requires the missing HR image details to be reconstructed. In this research, the long-established single-image super-resolution problem is addressed by integrating the multiresolution property of Wavelet and the flexibility of Locally Anchored Neighbourhood Regression model to formulate a novel edgebased single image super resolution algorithm that allows robust estimation of missing frequency details in wavelet domain with complete enhancement procedure. Firstly, the low resolution input image is decomposed into four frequency sub-bands, comprising of one approximate coefficient and three detailed coefficients sampled by applying discrete wavelet transformation. The underlying idea is to process and reconstruct information in low and high frequency sub-bands based on separable property of neighbourhood filtering to achieve fast parallel and vectorized operation, while enhancing algorithmic performance by reducing computational burden resulting from computing the weighted function of every pixel for each pixel in an image. We then processed the frequency sub-bands using the inverse discrete wavelet transforms which does not in any way increase image size, rather it reconstructs the original image with high integrity of preserved fine edge details and more realistic textures. Super resolution is then achieved using the regularized patch representation (projection matrix) learned to predict the high resolution image features. Lastly, we incorporate the nonlocal self-similarity prior to refine our reconstructed high resolution result; hence preserving the local singularity and edges details to achieve a more sophisticated, distinctive and robust image super resolution. Experimental results on standard images with qualitative and quantitative comparisons against several top-performing state- of-the-art SR methods demonstrate the effectiveness and stability of the proposed algorithm. The proposed method reaches the highest PSNR for scale factors of 2, 3 and 4, respectively for Set5 datasets with around 0.03- 0.70 dB better than LANR, and 0.2-1.60 dB better than the second best method, i.e. ANR. Similarly, we achieved around 0.03-1.10 dB better than LANR, and 0.2-1.80 dB better than ANR for scale factors of 2, 3 and 4 on Set14 dataset. High resolution imaging Image processing - Digital techniques Algorithms 2019-04 Thesis http://psasir.upm.edu.my/id/eprint/77392/ http://psasir.upm.edu.my/id/eprint/77392/1/FK%202019%201%20ir.pdf text en public masters Universiti Putra Malaysia High resolution imaging Image processing - Digital techniques Algorithms Sulaiman, Nasri
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Sulaiman, Nasri
topic	High resolution imaging Image processing - Digital techniques Algorithms
spellingShingle	High resolution imaging Image processing - Digital techniques Algorithms Somadina, Ike Chidiebere Super resolution imaging using modified lanr based on separable filtering
description	Recently, remarkable advances have been achieved in reconstructing high-resolution image from noisy, and low-resolution images. Reaching super resolution has been a challenge in image processing practices, because of their under-constrained nature that requires the missing HR image details to be reconstructed. In this research, the long-established single-image super-resolution problem is addressed by integrating the multiresolution property of Wavelet and the flexibility of Locally Anchored Neighbourhood Regression model to formulate a novel edgebased single image super resolution algorithm that allows robust estimation of missing frequency details in wavelet domain with complete enhancement procedure. Firstly, the low resolution input image is decomposed into four frequency sub-bands, comprising of one approximate coefficient and three detailed coefficients sampled by applying discrete wavelet transformation. The underlying idea is to process and reconstruct information in low and high frequency sub-bands based on separable property of neighbourhood filtering to achieve fast parallel and vectorized operation, while enhancing algorithmic performance by reducing computational burden resulting from computing the weighted function of every pixel for each pixel in an image. We then processed the frequency sub-bands using the inverse discrete wavelet transforms which does not in any way increase image size, rather it reconstructs the original image with high integrity of preserved fine edge details and more realistic textures. Super resolution is then achieved using the regularized patch representation (projection matrix) learned to predict the high resolution image features. Lastly, we incorporate the nonlocal self-similarity prior to refine our reconstructed high resolution result; hence preserving the local singularity and edges details to achieve a more sophisticated, distinctive and robust image super resolution. Experimental results on standard images with qualitative and quantitative comparisons against several top-performing state- of-the-art SR methods demonstrate the effectiveness and stability of the proposed algorithm. The proposed method reaches the highest PSNR for scale factors of 2, 3 and 4, respectively for Set5 datasets with around 0.03- 0.70 dB better than LANR, and 0.2-1.60 dB better than the second best method, i.e. ANR. Similarly, we achieved around 0.03-1.10 dB better than LANR, and 0.2-1.80 dB better than ANR for scale factors of 2, 3 and 4 on Set14 dataset.
format	Thesis
qualification_level	Master's degree
author	Somadina, Ike Chidiebere
author_facet	Somadina, Ike Chidiebere
author_sort	Somadina, Ike Chidiebere
title	Super resolution imaging using modified lanr based on separable filtering
title_short	Super resolution imaging using modified lanr based on separable filtering
title_full	Super resolution imaging using modified lanr based on separable filtering
title_fullStr	Super resolution imaging using modified lanr based on separable filtering
title_full_unstemmed	Super resolution imaging using modified lanr based on separable filtering
title_sort	super resolution imaging using modified lanr based on separable filtering
granting_institution	Universiti Putra Malaysia
publishDate	2019
url	http://psasir.upm.edu.my/id/eprint/77392/1/FK%202019%201%20ir.pdf
_version_	1747813220486217728

Super resolution imaging using modified lanr based on separable filtering

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