Search Results for author:
Found 6 papers, 2 papers with code
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse Representations
Hyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels.
Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations
This paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian noise, and fast hyperspectral inpainting (FastHyIn), an inpainting algorithm to restore HSIs where some observations from known pixels in some known bands are missing.
External Patch-Based Image Restoration Using Importance Sampling
This paper introduces a new approach to patch-based image restoration based on external datasets and importance sampling.
Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians
This paper proposes a general framework for internal patch-based image restoration based on Conditional Random Fields (CRF).
Class-specific image denoising using importance sampling
In this paper, we propose a new image denoising method, tailored to specific classes of images, assuming that a dataset of clean images of the same class is available.
Class-specific Poisson denoising by patch-based importance sampling
In this paper, we address the problem of recovering images degraded by Poisson noise, where the image is known to belong to a specific class.
