Non-Iterative Optimization of Pseudo-Labeling Thresholds For Training Object Detection Models From Multiple Datasets

The present work presents an artificial neural network architecture for the restoration of images damaged by underexposure and overexposure. The problem is relevant in computer vision applications that are applied in conditions where the limitation of the sensor prevents the scene details from being adequately represented in the captured image. This research presents an attention-based architecture composed of two convolutional neural networks, where one performs a pre-processing of the input image, while the other performs the restoration and enhancement of the degraded image. Regarding the evaluation of research results, a broad range of image quality metrics is used to assess the quality of the results produced by the model. The obtained results indicate that the proposed architecture is able to enhance images damaged by exposure heterogeneity, offering gains over state-of-art models in real data.