Computational imaging through chromatic aberration corrected simple lenses

Abstract

Modern camera lenses become increasingly more complex to optimize the light efficiency of the optical system. Recent research has combined single lens optics with complex post-capture correction methods based on computational photography. This study further improves lens design by correcting chromatic aberrations, after which a simple image deconvolution method is sufficient to produce a high-quality image. We initially estimate the point spread function with a blind image deconvolution method. We add Gaussian regularization as kernel prior to improve the accuracy of kernel estimation. A fast non-blind deconvolution method is then performed to recover a clear image. Experimental results show that the proposed method is at par with state-of-the-art deconvolution approaches. We found a better trade-off between lens design and digital image processing than previous work.

Keywords:

• Computational photography
• simple lens imaging
• chromatic aberration
• point spread function
• image deconvolution
• optimization

Notes

No potential conflict of interest was reported by the authors.