In this paper, we introduce a concept of the hybrid lens as a novel form of optical information processing apparatus that integrates the conventional optical lenses and the recently proposed neural lens, that is, an image post-processing technique based on generative convolution neural networks (GCNN). This integration is based on leveraging the fact that the Fourier plane is the common working principle of both types of lens. We demonstrate how manipulating the coherent light's spectral components on the Fourier plane behind a biconvex lens is a computationally-free alternative to performing convolution matrix operation in GCNN, which involves high computational expenses. In our approach, the GCNN can perform image generation in a shorter time. Our hybrid lens only requires computational power at the levels that the embedded resources on medical devices can afford. This feature is very important for commercialization as it allows making standalone units like microscopes without relying on external resources such as computing clouds.