STA-net: a deblurring network combined with spatiotemporal information for zinc froth flotation

Abstract The clarification of blurry frames in froth flotation videos is crucial for the identification and fault diagnosis of zinc flotation conditions. Irregular motion blur represents the primary blur phenomenon in froth flotation videos. Existing methods for motion blur removal have not effectively integrated spatio-temporal information, overlooking the utilization of clear frames, thus failing to achieve satisfactory results in the removal of froth flotation video blur frames. Therefore, this paper proposes a Spatio Temporal Awareness network (STA-net) capable of utilizing the temporal and spatial information from consecutive frames to naturally reconstruct froth images with clarity. This network consists of two sub-networks: the Temporal network (T-net) and the Spatio network (S-net). T-net extracts dynamic information between adjacent clear frames and blurry frames, while S-net captures spatial information within frames and enhances spatial texture details for the coarse images reconstructed based on the temporal information extracted by T-net in a layer-by-layer manner. Additionally, considering that blurry image datasets from actual froth flotation processes often lack corresponding clear images as training labels for the network, a method based on probability density functions for generating blurry froth image datasets based clear images is proposed. This method extracts motion vectors from froth images to create blurry froth images that closely resemble the actual blurry conditions of froth images. The deblurring performance of STA-net has been validated in practical froth flotation processes.