Explicit-restriction convolutional framework for lensless imaging

Mask-based lensless cameras break the constraints of traditional lens-based cameras, introducing highly flexible imaging systems. However, the inherent restrictions of imaging devices lead to low reconstruction quality. To overcome this challenge, we propose an explicit-restriction convolutional framework for lensless imaging, whose forward model effectively incorporates multiple restrictions by introducing the linear and noise-like nonlinear terms. As examples, numerical and experimental reconstructions based on the limitation of sensor size, pixel pitch, and bit depth are analyzed. By tailoring our framework for specific factors, better perceptual image quality or reconstructions with 4x pixel density can be achieved. This proposed framework can be extended to lensless imaging systems with different masks or structures. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

Mask-based lensless cameras offer flexible imaging systems by breaking the constraints of traditional lens-based cameras. However, the limitations of imaging devices result in low reconstruction quality. To address this challenge, we propose an explicit-restriction convolutional framework that effectively incorporates multiple constraints by introducing linear and nonlinear terms. By customizing the framework, better image quality or higher pixel density can be achieved.