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- W4322579459 abstract "Compressive Sensing (CS) is a scheme which focusses on breaking the limitation of Nyquist sampling rate, which is ideal for resource-constrained Internet of Underwater Things (IoUT). CS becomes inefficient for massive scale images with computational burden and vast storage. To provide better sampling performance, block measurement of images can be considered. It produces block distortion and fade after reconstruction. To overcome this problem, we propose an Adaptive Block Compressed Sensing (ABCS) technique that is based on the energy content of the image blocks. Higher number of samples are alloted to blocks with more energy and vice versa. Simulation is performed on underwater images and experimental results reveal that the proposed algorithm improved the quality of reconstructed image significantly." @default.
- W4322579459 created "2023-02-28" @default.
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- W4322579459 date "2023-01-01" @default.
- W4322579459 modified "2023-09-26" @default.
- W4322579459 title "Adaptive measurement allocation for underwater images using block energy in haar wavelet domain" @default.
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- W4322579459 doi "https://doi.org/10.1063/5.0101135" @default.
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