Aiming at the problems of uneven UV spatial frequency sampling and inverse Fourier transform (IFT) artifacts of the photonic integrated interference imaging system, this study proposes a new imaging system based on a front-end S-shaped microlens array, combined with a conditional denoising diffusion probabilistic model (Con-DDPM). The front-end S-shaped microlens array improves the uniformity of UV spatial frequency sampling, increasing the average peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) by approximately 5 dB and 0.16, respectively. In addition, the deep learning reconstruction algorithm based on Con-DDPM is employed to process the IFT images. This algorithm effectively removes artifacts and restores the detailed information of the images. As a result, the average PSNR and SSIM are improved by approximately 9 dB and 0.38, respectively. These enhancements have significantly improved the imaging quality, laying a solid foundation for the future development of space-based telescopes.