Efficient denoising of cold atom images using the optimized eigenface recognition algorithm

Absorption imaging is a widely employed technique for detecting cold atom clouds and Bose-Einstein condensates (BECs). There are situations where such images may suffer from unwanted interference fringes, resulting in uncertainties in determining crucial parameters such as the atom number, temperatures, or even dynamics in small timescales. Reducing the acoustic vibrations and recording image frames synchronized with the source of such vibrations can largely reduce these fringes; however, some residual fringes still need to be taken care of for precision measurements. In this study, we propose an efficient image post-processing technique for noise reduction that effectively mitigates such interference patterns. Our approach makes use of the well-known eigenface recognition algorithm, combined with an optimized masking strategy applied to the image of the atomic cloud using a small number of basis sets. The use of a limited basis set ensures minimal computational time, allowing this method to be readily incorporated into every experimental run. Through the application of our technique, we successfully reduce interference fringes and improve the accuracy of parameter estimation by 50% in the absorption imaging of cold atoms. The temperature uncertainties of cold ⁸⁷ R b atoms are reduced by more than 50% after the algorithm is applied. This approach holds significant promise for enhancing the reliability and precision of experimental measurements in diverse research fields where absorption imaging is employed.