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Weighted Image Reconstruction Algorithm Based on Adaptive Sampling Rate

Qiang Fan, Dongjian He


To solve the problems of poor-quality reconstruction and waste of resources when compressing image samples based on a fixed sampling rate, a weighted image reconstruction algorithm based on adaptive sampling rate is proposed in this research. The algorithm first automatically selects a suitable sampling rate based on the sparseness of image blocks, and then measures and reconstructs the images by means of observation matrix weighted BCS. An experiment was done with 64 gray test images. The result reveals that at a same sampling rate the PSNR of the reconstructed images by the proposed algorithm has increased an average of 5.63dB compared with that by the conventional BCS. Thus this proposed algorithm proves to effectively improve the quality of the reconstructed image.


Compressive Sensing, Adaptive sampling rate, Observation matrix, Reconstructed images.

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