CLEAN Algorithm In Reconstructed Image

Reconstructing images is an important direction in the field of astronomy. Interference mapping in radio telescope, due to uv incomplete coverage, then caused image quality is not high, and we need to refactoring through CLEAN algorithm to improve image quality. Due to the limitations of the traditional CLEAN algorithm, its application is limited to the isolated point source, but there may be other cases, such as point source and extended source are mixed.CLEAN algorithm on the astronomical aspects, radar detection and other fields has a great influence, then we need to Adjust the CLEAN to more environments, in order to make CLEAN algorithm more widely used, then the expansion of this algorithm is very necessary.In this paper,the origin of CLEAN algorithm is described. The principle of interference mapping of radio telescope is introduced briefly. The relationship between "dirty map", "dirty beam" and "clean map" is introduced. CLEAN algorithm needs to solve the problem, but also some of the existing CLEAN expansion methods were investigated. In this paper, we propose a CLEAN iteration by weakened the effective beam, and the target quality is used to control the number of iterations, and the iteration can be terminated when the error convergence is reached.Finally,the algorithm is validated by one-dimensional simulation. The algorithm proposed in this paper is also good for the point source, and also has a good effect in the case of point source and source mixing.