Compressed Sensing SAR Imaging with Multilook Processing
This is an incremental improvement for synthetic aperture radar imaging, addressing a specific bottleneck in compressive sensing applications.
The paper tackled the problem of applying multilook processing for speckle reduction in compressive sensing SAR imaging, where sub-Nyquist sampling causes aliasing that hinders existing techniques, and proposed a method that integrates multilook into reconstruction to achieve efficient sparse scene recovery and comparable speckle reduction.
Multilook processing is a widely used speckle reduction approach in synthetic aperture radar (SAR) imaging. Conventionally, it is achieved by incoherently summing of some independent low-resolution images formulated from overlapping subbands of the SAR signal. However, in the context of compressive sensing (CS) SAR imaging, where the samples are collected at sub-Nyquist rate, the data spectrum is highly aliased that hinders the direct application of the existing multilook techniques. In this letter, we propose a new CS-SAR imaging method that can realize multilook processing simultaneously during image reconstruction. The main idea is to replace the SAR observation matrix by the inverse of multilook procedures, which is then combined with random sampling matrix to yield a multilook CS-SAR observation model. Then a joint sparse regularization model, considering pixel dependency of subimages, is derived to form multilook images. The suggested SAR imaging method can not only reconstruct sparse scene efficiently below Nyquist rate, but is also able to achieve a comparable reduction of speckles during reconstruction. Simulation results are finally provided to demonstrate the effectiveness of the proposed method.