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Found 8 papers, 0 papers with code
Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures
Model-based deep learning methods such as \emph{loop unrolling} (LU) and \emph{deep equilibrium model} (DEQ) extensions offer outstanding performance in solving inverse problems (IP).
Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects
The contributions of this paper lie in its comprehensive survey of ML-based works in the ISAC domain and its identification of challenges and future research directions.
Review of Contemporary Energy Harvesting Techniques and Their Feasibility in Wireless Geophones
However, due to the random and intermittent nature of the harvested energy, it is important that geophones must be equipped to tap from several energy sources for a stable operation.
Learned Proximal Operator for Solving Seismic Deconvolution Problem
Due to the sparse nature of the reflectivity sequence, spike-promoting regularizers such as the $\ell_1$-norm are frequently used.
Details Preserving Deep Collaborative Filtering-Based Method for Image Denoising
Extensive experiments show that the DeepCoFiB performed quantitatively (in terms of PSNR and SSIM) and qualitatively (visually) better than many of the state-of-the-art denoising algorithms.
Dense-Sparse Deep CNN Training for Image Denoising
We extend this training approach to a reduced DnCNN (RDnCNN) network resulting in a faster denoising network with significantly reduced parameters and comparable performance to the DnCNN.
Collaborative Filtering-Based Method for Low-Resolution and Details Preserving Image Denoising
Many also distort images of lower resolution resulting in a partial or complete structural loss.
Image Denoising Via Collaborative Support-Agnostic Recovery
For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the same group.
