End-to-end learned single lens design using improved Wiener deconvolution

Abstract

End-to-end single-lens imaging system design is a method to optimize both optical system and reconstruction algorithm. Most end-to-end single lens systems use convolutional neural networks (CNN) for image restoration, which fit the transformation relationship between the aberrated image and the ground truth image in the training set. Based on the principle of optical imaging, we realize non-blind image restoration through Wiener deconvolution. Wiener deconvolution is improved with the powerful fitting ability of depth learning so that the noise parameters and the blur kernel in Wiener deconvolution can be simultaneously optimized with the optical parameters in the lens. Extensive comparative tests have been conducted to demonstrate the single-lens imaging system obtained by our method has more stable imaging quality and a 40 times greater imaging speed than the method using CNN restoration algorithm.

Comparison of model-based and data-based co-design of phase masks for depth of field extension

“…Except from the work of Zhang et al 12 where an improved Wiener filter with a learned kernel has been proposed, to the best of our knowledge, comparison or combination of data-based and model-based co-design has not been deeply investigated for a given task. This paper is a preliminary work towards the investigation of the comparison of model-based and data-based co-design.…”

Embedded Processing for Extended Depth of Field Imaging Systems: From Infinite Impulse Response Wiener Filter to Learned Deconvolution

“…This means that digital processing parameters depend on optical system parameters, but it also means that optical system parameters depend on digital processing. Among the different digital processing techniques, neural networks can be used [3][4][5][6][7][8][9]. However, some applications (e.g., advanced driver assistance systems) require real-time digital processing, with a very limited memory storage ability.…”