The electromagnetic detection and imaging system enables wide-range, wideband, and fast localization of electromagnetic interference sources. The system primarily consists of a parabolic reflector and a multi-channel ultra-wideband signal acquisition system. Due to variations in device parameters across channels caused by manufacturing processes, it is impossible to achieve complete consistency, resulting in stripe noise in the obtained electromagnetic images that significantly affects localization accuracy. A bidirectional gated recurrent unit (BiGRU)-convolutional neural network (CNN) model was constructed, which constructs a dataset based on the measured data as the input. The BiGRU and the CNN utilize the strong correlation between neighboring rows of the image to extensively collect redundant information from the past and future inputs, to extract the stripe noise and to integrate the spatial information, and to utilize the difference between the data for loop iteration of this process. The model is validated through a large number of experiments and the BiGRU-CNN method outperforms other tested (classical) methods by reducing the vertical gradient energy by 15.2% and the residual nonuniformity by 21.9%.