Development of cubic freeform optical surface for wavefront coding application for extended depth of field Infrared camera

Pant, L. M.; Singh, Mahendra; Chandra, Kanchan; Mishra, Vinod; Pandey, Neeraj; Pant, Kamal K.; Khan, Gufran S.; Sakher, Chandra

doi:10.1016/j.infrared.2022.104377

Cited by 4 publications

(1 citation statement)

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“…This drastically limits the weight of the restoration algorithm that can be used. Yet, in an end-to-end, optical/digital design context for DoF extension, the PSFs of the optical system are often large [1,19,20], which can be problematic if one wishes to deconvolve with a FIR filter, especially with a very small kernel.…”

Section: Introductionmentioning

confidence: 99%

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

Fontbonne,

Trouvé-Peloux,

Champagnat

et al. 2023

Sensors

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Many works in the state of the art are interested in the increase of the camera depth of field (DoF) via the joint optimization of an optical component (typically a phase mask) and a digital processing step with an infinite deconvolution support or a neural network. This can be used either to see sharp objects from a greater distance or to reduce manufacturing costs due to tolerance regarding the sensor position. Here, we study the case of an embedded processing with only one convolution with a finite kernel size. The finite impulse response (FIR) filter coefficients are learned or computed based on a Wiener filter paradigm. It involves an optical model typical of codesigned systems for DoF extension and a scene power spectral density, which is either learned or modeled. We compare different FIR filters and present a method for dimensioning their sizes prior to a joint optimization. We also show that, among the filters compared, the learning approach enables an easy adaptation to a database, but the other approaches are equally robust.

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Section: Introductionmentioning

confidence: 99%