反褶积
镜头(地质)
光学
光学(聚焦)
计算机科学
点扩散函数
点(几何)
计算机视觉
对象(语法)
盲反褶积
人工智能
基点
算法
物理
数学
几何学
作者
Praveen Periyasamy Angamuthu,Francis Gracy Arockiaraj,Shivasubramanian Gopinath,Daniel Smith,Tauno Kahro,Sandhra-Mirella Valdma,Andrei Bleahu,Soon Hock Ng,Andra Naresh Kumar Reddy,Tomas Katkus,Aravind Simon John Francis Rajeswary,R. A. Ganeev,Siim Pikker,Kaupo Kukli,Aile Tamm,Saulius Juodkazis,Vijayakumar Anand
出处
期刊:Photonics
[MDPI AG]
日期:2022-08-31
卷期号:9 (9): 625-625
被引量:29
标识
DOI:10.3390/photonics9090625
摘要
A refractive lens is one of the simplest, most cost-effective and easily available imaging elements. Given a spatially incoherent illumination, a refractive lens can faithfully map every object point to an image point in the sensor plane, when the object and image distances satisfy the imaging conditions. However, static imaging is limited to the depth of focus, beyond which the point-to-point mapping can only be obtained by changing either the location of the lens, object or the imaging sensor. In this study, the depth of focus of a refractive lens in static mode has been expanded using a recently developed computational reconstruction method, Lucy-Richardson-Rosen algorithm (LRRA). The imaging process consists of three steps. In the first step, point spread functions (PSFs) were recorded along different depths and stored in the computer as PSF library. In the next step, the object intensity distribution was recorded. The LRRA was then applied to deconvolve the object information from the recorded intensity distributions during the final step. The results of LRRA were compared with two well-known reconstruction methods, namely the Lucy-Richardson algorithm and non-linear reconstruction.
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