Yuxing Mao
Chongqing University, China
Title: Super-Resolution image reconstruction from multiple defocused images of stationary scene
Biography
Biography: Yuxing Mao
Abstract
Super-resolution reconstruction (SRR) is an effective means to address the problem of insufficient image resolution in imaging applications. Existing SRR algorithms use well-focused images and ignore the value of defocused images generated by the imaging system during focusing. The starting point of the present study is to treat a defocused image as distribution and accumulation of scene information among different pixels of the detector, as well as a valid observation of the imaged subject; defocused images are the result of blurring a corresponding high resolution (HR) image using a point spread function (PSF) followed by downsampling. From this starting point, we used multiple defocused images to build an observation model for HR images and propose a SRR algorithm to approach the HR images. We have developed an image degradation model by analyzing optical lens imaging, used the particle swarm optimization (PSO) algorithm to estimate the PSF of the HR image, and used compressed sensing (CS) theory to implement SRR based on the non-coherent characteristics of multiple defocused images. Experiments demonstrate that our method can be used to obtain more information about details of a scene and improve the visual effect without adding any hardware facilities, improving the recognition and interpretation of the image subject.
