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: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/77392/1/FK%202019%201%20ir.pdf |
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| Summary: | 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. |
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