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Found 6 papers, 3 papers with code
Guided Nonlocal Patch Regularization and Efficient Filtering-Based Inversion for Multiband Fusion
In multiband fusion, an image with a high spatial and low spectral resolution is combined with an image with a low spatial but high spectral resolution to produce a single multiband image having high spatial and spectral resolutions.
Plug-and-Play Regularization using Linear Solvers
Coupled with a model-based loss function, these are typically used for image reconstruction within an optimization framework.
Averaged Deep Denoisers for Image Regularization
In this work, we construct contractive and averaged image denoisers by unfolding splitting-based optimization algorithms applied to wavelet denoising and demonstrate that their regularization capacity for PnP and RED can be matched with CNN denoisers.
Fixed-Point and Objective Convergence of Plug-and-Play Algorithms
A standard model for image reconstruction involves the minimization of a data-fidelity term along with a regularizer, where the optimization is performed using proximal algorithms such as ISTA and ADMM.
Fast High-Dimensional Kernel Filtering
We demonstrate the effectiveness of our proposal for bilateral and nonlocal means filtering of color and hyperspectral images.
Fast High-Dimensional Bilateral and Nonlocal Means Filtering
Unlike existing approaches, where the focus is on approximating the data (using quantization) or the filter kernel (via analytic expansions), we locally approximate the kernel using weighted and shifted copies of a Gaussian, where the weights and shifts are inferred from the data.
