Infrared image super resolution with structure prior from uncooled infrared readout circuit.

Infrared images, rich in temperature information, have a broad range of applications. However, limitations in infrared imaging mechanisms and the manufacturing processes typically prevent uncooled infrared detector arrays from exceeding a resolution of one megapixel. Consequently, designing an efficient infrared image Super-Resolution (SR) algorithm is of significant importance. In this paper, we draw inspiration from the readout circuit structure prior to Uncooled Infrared Focal Plane Arrays (IRFPA), which scans infrared signals row by row and reads them out column by column. We propose an efficient Row-Column Transformer Block (RCTB) that splits features into rows and columns to effectively capture the spatio-temporal correlation between row-column pixels. Acknowledging the continuity of temperature information within the image and the correlation between adjacent pixel regions, we develop a Compact Convolution Block (CCB) that incorporates a U-shape Spatial Channel Attention Block (USCAB) to extract local features before the RCTB. During the training phase, we introduce a regularization control term into the loss function to enhance the reconstruction details of the infrared image. Building on these components, we propose a hybrid network named Efficient Infrared Image Super-Resolution (EIRSR), which achieves an excellent balance between performance and efficiency in terms of parameters and computational costs. Furthermore, we scale the model to create a family of variants, collectively known as EIRSRs. Extensive experiments demonstrate that EIRSR delivers competitive performance while maintaining a compact structure compared to superior methods.