Convolutional Sparse

Convolutional sparse coding (CSC) focuses on representing signals, particularly images, as sparse combinations of learned convolutional filters, aiming for efficient and interpretable representations. Current research explores CSC's application in diverse areas, including image enhancement, scene analysis, and signal recovery, often integrating it with deep learning architectures like autoencoders and transformer networks or employing algorithms such as proximal gradient methods and alternating direction method of multipliers. This approach offers advantages in computational efficiency, improved robustness to noise and adversarial attacks, and enhanced interpretability compared to purely black-box deep learning models, impacting fields ranging from remote sensing to medical imaging.