Abstract: An image processing method including acquiring a first image whose spatial resolution and lightness are to be enhanced; generating a residual image from the first image using a multi-scale hierarchical neural network for joint learning of low-light enhancement and super-resolution, the network comprising an encoder stage and a decoder stage forming a plurality of symmetrical encoder-decoder levels, each encoder and decoder in each level comprising a vision transformer block; generating a reconstructed image based on the first and residual images.

Abstract: A method and system for identifying, using a computing unit, lengths of a particle in a material flow is disclosed. The computing unit is configured to analyze images of the material flow (13) in an analytical routine and to derive particle lengths of particles of the material flow contained in the images. The particle length derived in the analytical routine may comprise an excess length, with the analytical routine being based on a machine learning method trained to find or identify particles with the excess length.

Abstract: A method and system for identifying lengths of a particle in a flow of harvested material comprising particles in an agricultural harvester is disclosed. The agricultural harvester has at least one work assembly for harvesting a crop or for processing harvested material of the crop and can be adjusted using machine parameters. The harvested material is transported as a flow of harvested material through the agricultural harvester while the agricultural harvester is operating. The agricultural harvester has a camera that takes images of the flow of harvested material, with a computing unit of the agricultural harvester analyzing the images of the flow of harvested material in an analytical routine thereby finding particle lengths of particles of the flow of harvested material that are an excess length.