Mitigating Structural Fragmentation in Sonar Deconvolution: A Directional Total Variation Approach With FISTA Acceleration

Underwater 3-D imaging systems face critical challenges in achieving high-resolution reconstructions owing to physical aperture limitations and computational complexity. Conventional beamforming methods suffer from wide mainlobes and elevated sidelobes, whereas existing deconvolution approaches, though effective in super-resolution recovery of discrete point targets, tend to oversegment contiguous scattering bodies into sparse point clusters under operational conditions, leading to structural fragmentation and compromised target detectability. This study proposes a deconvolution framework for underwater targets through a hybrid regularization combining total variation and L1-norm constraints. The comprehensive validation demonstrates superior performance in continuous structural reconstruction, with experimental results showing an improvement in the structural similarity index compared to other deconvolution methods. Field tests conducted in marine environments further validate the framework’s ability to suppress sidelobe artefacts while maintaining contour integrity, thus establishing its potential for high-precision underwater detection and mapping applications.