  We present a new approach leveraging the Sliding Frank--Wolfe algorithm to
address the challenge of line recovery in degraded images. Building upon
advances in conditional gradient methods for sparse inverse problems with
differentiable measurement models, we propose two distinct models tailored for
line detection tasks within the realm of blurred line deconvolution and ridge
detection of linear chirps in spectrogram images.
