Pareto optimality between width of central lobe and peak sidelobe intensity in the far-field pattern of lossless phase-only filters for enhancement of transverse resolution

Abstract

Resolution capability of an optical imaging system can be enhanced by reducing the width of the central lobe of the point spread function. Attempts to achieve the same by pupil plane filtering give rise to a concomitant increase in sidelobe intensity. The mutual exclusivity between these two objectives may be considered as a multiobjective optimization problem that does not have a unique solution; rather, a class of trade-off solutions called Pareto optimal solutions may be generated. Pareto fronts in the synthesis of lossless phase-only pupil plane filters to achieve superresolution with prespecified lower limits for the Strehl ratio are explored by using the particle swarm optimization technique.

Analysis of Strehl ratio limit with superresolution binary phase filters

“…where ρ 1 is the normalized radius of the first zone (note that a similar expression for n-zone phase filters can be found in Ref. [22]). The core width is given by the position of the first zero…”

“…Furthermore, the comparison of 0-π filters with complex filters is discussed. Finally, resolution improvement gives rise not only to a Strehl decrease, but also to an increase in the sidelobe intensity [22] . In most applications, the sidelobe height is as crucial as the Strehl ratio and the resolution to make a technique viable.…”

Binary diffractive lens with subwavelength focusing for terahertz imaging

“…According to Eq. ( 1), this diffractive element with proper structure is available to modulate the point spread function within the size of an Airy spot, thus providing a focusing capability of subwavelength magnitude [26,27] and even manipulating the depth of focus [26,28,29] . The radii of the rings are related with the optimization of the cost functions in simulated annealing algorithm, which is a heuristic random search process based on the solution strategy of Monte Carlo iteration [28,30] .…”