Victor Sineglazov¹ and Kyrylo Lesohorskyi², ¹Kyiv Aviation Institute, Ukraine, ²National Technical
University of Ukraine, Ukraine

This work is devoted to the modification of existing blind image restoration algorithms and methodologies for noise and blur elimination in videos and images captured by unmanned aerial vehicles. This work improves on the existing algorithms and methodologies to address the challenges and limitations of existing tools when applied to high-dimensional hyperspectral data by applying channel compression based on 3d convolutions as a dimensionality reduction method. The methods and algorithms described in this paper can be applied in near-real-time and batch-processing scenarios. A detailed analysis of noise and blur types and their respective sources is provided. An overview of existing methods is given, and their limitations when applied to hyperspectral data are analyzed. A two-stage image restoration approach for hyperspectral data based on is introduced. Proposed algorithms solve the key limitations of hyperspectral data image restoration, providing quality and performance, comparable to non-hyperspectral image restoration.

Hyperspectral Imagery, Image Restoration, Recurrent Neural Networks, Unmanned Aerial Vehicles