In order to improve the quality of photoacoustic image reconstruction, aiming at the problems that the signal-to-noise ratio of the original photoacoustic signal is poor, the reconstructed image contrast is low and the resolution is insufficient in the process of photoacoustic image reconstruction, based on the quality of photoacoustic signal which collected from the optimized photoacoustic imaging system, a reconstructed filtering algorithm in view of Renyi entropy is proposed(before using the delay superposition algorithm to reconstruct the image, the original photoacoustic signal is filtered by Renyi entropy filter). Compared with the existing classical filtering algorithm (modulus maxima method and threshold denoising method), the contrast ratio of the algorithm is improved by 18.27% on average, the resolution is increased by 23.69% on average, the SNR is increased by 2.90% on average, and the mean square error is reduced by 2.61% on average by using Renyi algorithm. The photoacoustic signal of the pencil (zero-dimension), hair (one dimension) and mouse cortical blood vessels (two-dimensional) was filtered by the Renyi entropy filter before performing the photoacoustic image reconstruction. After Renyi entropy filtering, the contrast of the photoacoustic reconstructed images was greatly improved by 36.75% (pencil cross section, zero dimension), 30.22% (hairline, one dimension) and 30.38% (mouse cortical blood vessels, two-dimensional). The resolution of reconstructed images also increased significantly, but the resolution of mouse cortical blood vessels was limited (17.65%) compared with zero-dimensional and one-dimensional samples. We speculate that this is related to the selection of biological samples (the first two samples were imitation, the samples of mouse cortical blood vessels were in vivo, and the differences in the photoacoustic signals between the mouse cortical blood vessels and the surrounding biological tissues were weaker than those). The signal-to-noise ratio of reconstructed images was significantly increased by 43.20% (pencil cross section, zero dimension), 51.60% (hairline, one dimension) and 48.20% (mouse cortical blood vessels, two dimensions). Finally, the mean square error of reconstructed images decreased by 7.10% (pencil core cross section, zero dimension), 28.57% (hairline, one dimension) and 69.38% (mouse cortical blood vessels, two dimensions), with the increase of the sample dimension, the mean square error of the image is greatly reduced. We assume that this is due to the increase in the size of the sample with the increase of the sample, and the average error of the whole image is reduced, so that the mean square error corresponding to the reconstructed image is reduced.
The experimental results show that the reconstructed images of the photoacoustic reconstructed by Renyi entropy compared with the reconstructed images obtained from the original photoacoustic signal, the contrast ratio of the photoacoustic reconstructed image is enhanced by 32.45%, the resolution is increased by 30.78% and the signal-to-noise ratio is increased by 47.66%, and the mean square error is reduced by 35.01%. The Renyi entropy filter processing algorithm improves the quality of photoacoustic image reconstruction which will help to promote the clinical application of photoacoustic imaging in biomedical diagnosis and treatment, for example, the early diagnosis about the arthritis, breast cancer and epilepsy and other lesions.