2021/06/29 by Deborah Pereg, Israel Cohen, Anthony A. Vassiliou +1 · 7 citations
Computer Science · Earth and Planetary Sciences · Engineering · #Algorithm #Artificial intelligence #Computer science #Convolutional neural network #Feature (linguistics) #Image and Signal Denoising Methods #Neural coding #Pattern recognition (psychology) #Seismic Imaging and Inversion Techniques #Solver #Sparse and Compressive Sensing Techniques #Sparse matrix #cs.CE #cs.CV #cs.LG
paper · pdf · doi:10.1109/tgrs.2021.3105300
published in IEEE Transactions on Geoscience and Remote Sensing 60, 1-19 (Institute of Electrical and Electronics Engineers)
arxiv created 2021/06/29 · openalex created_date 2021/07/05 · openalex publication_date 2021/08/26 · arxiv updated 2022/02/16 · openalex updated_date 2026/08/05
In sparse coding, we attempt to extract features of input vectors, assuming that the data is inherently structured as a sparse superposition of basic building blocks. Similarly, neural networks perform a given task by learning features of the training dataset. Recently, both data- and model-driven feature extracting methods have become extremely popular and have achieved remarkable results. Nevertheless, practical implementations are often too slow to be employed in real-life scenarios, especially for real-time applications. We propose a speed-up upgraded version of the classic iterative thresholding algorithm (ITA), which produces a good approximation of the convolutional sparse code (CSC) within 2–5 iterations. The speed advantage is gained mostly from the observation that most solvers are slowed down by inefficient global thresholding. The main idea is to normalize each data point by the local receptive field energy, before applying a threshold. This way, the natural inclination toward strong feature expressions is suppressed, so that one can rely on a global threshold that can be easily approximated, or learned during training. The proposed algorithm can be employed with a known predetermined dictionary, or with a trained dictionary. The trained version is implemented as a neural net designed as the unfolding of the proposed solver. The performance of the proposed solution is demonstrated via the seismic inversion problem in both synthetic and real data scenarios. We also provide theoretical guarantees for a stable support recovery, namely we prove that under certain conditions, the true support is perfectly recovered within the first iteration.