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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
http://psasir.upm.edu.my/id/eprint/77392/
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spelling my.upm.eprints.773922022-01-28T02:16:07Z http://psasir.upm.edu.my/id/eprint/77392/ Super resolution imaging using modified lanr based on separable filtering 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. 2019-04 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/77392/1/FK%202019%201%20ir.pdf Somadina, Ike Chidiebere (2019) Super resolution imaging using modified lanr based on separable filtering. Masters thesis, Universiti Putra Malaysia. High resolution imaging Image processing - Digital techniques Algorithms
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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
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
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/77392/1/FK%202019%201%20ir.pdf
http://psasir.upm.edu.my/id/eprint/77392/
_version_ 1724075570989367296
score 13.211869

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