Authors:
Muhammad Haris
and
Hajime Nobuhara
Affiliation:
University of Tsukuba, Japan
Keyword(s):
Sparse Representation, Edge Orientation, Super-resolution, Multiple Dictionaries, Gradient, High-frequency Component.
Abstract:
In this paper, we propose a new edge-aware super-resolution algorithm based on sparse representation via multiple
dictionaries. The algorithm creates multiple pairs of dictionaries based on selective sparse representation.
The dictionaries are clustered based on the edge orientation that categorized into 5 clusters: 0, 45, 90, 135, and
non-direction. The proposed method is conceivably able to reduce blurring, blocking, and ringing artifacts in
edge areas, compared with other methods. The experiment uses 900 natural grayscale images taken from USC
SIPI Database. It is confirmed that our proposed method is better than current state-of-the-art algorithms. To
amplify the evaluation, we use four evaluation indexes: higher peak signal-to-noise ratio (PSNR), structural
similarity (SSIM), feature similarity (FSIM) index, and time. On 3x magnification experiment, our proposed
method has the highest value for all evaluation compare to other methods by 11%, 14%, 6% in terms of PSNR,
SSIM, and F
SIM respectively. It is also proven that our proposed method has shorter execution time compare
to other methods.
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