Sparsifying Transform

Sparsifying transforms aim to represent signals efficiently by finding sparse representations in a transformed domain, improving data processing and reconstruction. Current research focuses on learning these transforms, rather than relying on fixed ones like wavelets or DCT, using deep learning architectures such as autoencoders and iterative neural networks to optimize for both sparsity and numerical stability. This allows for improved performance in applications like seismic imaging and medical image reconstruction, surpassing traditional methods by achieving faster convergence and better reconstruction quality, particularly in handling ill-posed inverse problems.