Research on Fabric Image Acquisition Based on Compressed Sensing

Ji Cheng Dong; Sheng Qi Guan; Long Long Chen

doi:10.4028/www.scientific.net/AMR.989-994.3698

Paper Titles

Study on Deep Structure of Extreme Learning Machine (DS-ELM) for Datasets with Noise
p.3679

Design of 2_DOF Simulation Platform for the Testing of Airborne Laser Communication APT System
p.3683

The Method of Optimal Group Based on the Kernel Density Estimation and the Close Degree
p.3689

Research of Frequency-Hopping Communication for Arbitrary Array Based on Adaptive Beam-Forming
p.3693

Research on Fabric Image Acquisition Based on Compressed Sensing
p.3698

Adaptive Equalization with Variable Step Size Algorithm Based on Gradient for HF Channel
p.3702

Joint Estimation of Doppler and Time Delay of Wideband Echoes for LFM Pulse Radar Based on Fractional Correlation in Impulse Noise Environment
p.3706

Parameter Estimation Based on Fractional Correlation under Alpha-Stable Distribution Noise Environment in Bistatic MIMO Radar System
p.3710

Multi-View Video Coding Based on Video Correction
p.3714

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Research on Fabric Image Acquisition Based on Compressed Sensing

Abstract:

Amount of data in collecting data of fabric image in the textile industry put forward a new challenge to sensor end. Compressed Sensing (CS) breaks limit of conventional Shannon’s sampling theorem, so we can reconstruct a signal in Sub-sampling rate. In addition, theoretical analysis tells us that collecting the fabric image data by CS method have a better advantage than collecting the general image data. Having reconstructed three fabric images and one general image by CS method, we can easily find that the former have a high quality of reconstruction.