TY - GEN
T1 - A Preliminary Comparison Between Compressive Sampling and Anisotropic Mesh-Based Image Representation
AU - Li, Xianping
AU - Wu, Teresa
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Compressed sensing (CS) has become a popular field in the last two decades to represent and reconstruct a sparse signal with much fewer samples than the signal itself. Although regular images are not sparse on their own, many can be sparsely represented in wavelet transform domain. Therefore, CS has also been widely applied to represent digital images. However, an alternative approach, adaptive sampling such as mesh-based image representation (MbIR), has not attracted as much attention. MbIR works directly on image pixels and represents the image with fewer points using a triangular mesh. In this paper, we perform a preliminary comparison between the CS and a recently developed MbIR method, AMA representation. The results demonstrate that at the same sample density, AMA representation can provide better reconstruction quality than CS based on the tested algorithms. Further investigation with recent algorithms is needed to perform a thorough comparison.
AB - Compressed sensing (CS) has become a popular field in the last two decades to represent and reconstruct a sparse signal with much fewer samples than the signal itself. Although regular images are not sparse on their own, many can be sparsely represented in wavelet transform domain. Therefore, CS has also been widely applied to represent digital images. However, an alternative approach, adaptive sampling such as mesh-based image representation (MbIR), has not attracted as much attention. MbIR works directly on image pixels and represents the image with fewer points using a triangular mesh. In this paper, we perform a preliminary comparison between the CS and a recently developed MbIR method, AMA representation. The results demonstrate that at the same sample density, AMA representation can provide better reconstruction quality than CS based on the tested algorithms. Further investigation with recent algorithms is needed to perform a thorough comparison.
KW - AMA representation
KW - Compressive sampling
KW - Mesh-based image representation
KW - PSNR
KW - Structural similarity index measure
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U2 - 10.1007/978-3-030-80119-9_57
DO - 10.1007/978-3-030-80119-9_57
M3 - Conference contribution
AN - SCOPUS:85112584886
SN - 9783030801182
T3 - Lecture Notes in Networks and Systems
SP - 876
EP - 885
BT - Intelligent Computing - Proceedings of the 2021 Computing Conference
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - Computing Conference, 2021
Y2 - 15 July 2021 through 16 July 2021
ER -