Gridless 2D Recovery of Lines using the Sliding Frank-Wolfe Algorithm
This work addresses line detection challenges in image processing, but appears incremental as it builds on existing conditional gradient methods.
The paper tackles the problem of line recovery in degraded images by proposing a new approach using the Sliding Frank-Wolfe algorithm, resulting in tailored models for blurred line deconvolution and ridge detection of linear chirps in spectrograms.
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.