Abstract

Due to the difficulty of correcting chromatic aberration (CA) in telephoto cameras, recent studies have combined image algorithms with simple optical structures, such as single-spherical lenses, for high-quality photography, moving away from complex optics. However, this approach often struggles to comprehensively address compounded issues arising from optical aberrations of simple optical systems, including defocus blur and multi-channel misalignment. To tackle this challenge, this manuscript presents an approach for developing a telephoto imaging system by leveraging the distinct characteristics of axial and lateral chromatic aberrations (ACA, LCA) over the visible spectrum. The optical design is limited to a specific wavelength range to preserve high-frequency information of the green channel. A cross-channel fitting method is presented to suppress the LCA. Subsequently, the powerful capabilities of deep learning are utilized to correct ACA, defocus blur, and other residual optical aberrations. Simulation experiments demonstrate the effectiveness of the proposed approach in mitigating the CA inherent in telephoto systems, thereby delivering high-quality imaging results over the whole visible waveband.

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