A Physics-Informed Low-Rank Deep Neural Network for Blind and Universal Lens Aberration Correction

High-end lenses, although offering high-quality images, suffer from both insufficient affordability and bulky design, which hamper their applications in low-budget scenarios or on low-payload platforms. A flexible scheme is to tackle the optical aberration of low-end lenses computationally. However, it is highly demanded but quite challenging to build a general model capable of handling non-stationary aberrations and covering diverse lenses, especially in a blind manner. To address this issue, we propose a universal solution by extensively utilizing the physical properties of camera lenses: (i) reducing the complexity of lens aberrations, i.e., lens-specific non-stationary blur, by warping annual-ringshaped sub-images into rectangular stripes to transform non-uniform degenerations into a uniform one, (ii) building a low-dimensional non-negative orthogonal representation of lens blur kernels to cover diverse lenses; (iii) designing a decoupling network to decompose the input low-quality image into several components degenerated by above kernel bases, and applying corresponding pre-trained deconvolution networks to reverse the degeneration. Benefiting from the proper incorporation of lenses' physical properties and unique network design, the proposed method achieves superb imaging quality, wide applicability for various lenses, high running efficiency, and is totally free of kernel calibration. These advantages bring great potential for scenarios requiring lightweight high-quality photography.