Attention-Aware Learning for Hyperparameter Prediction in Image Processing Pipelines.

Between the imaging sensor and the image applications, the hardware image signal processing (ISP) pipelines reconstruct an RGB image from the sensor signal and feed it into downstream tasks. The processing blocks in ISPs depend on a set of tunable hyperparameters that have a complex interaction with the output. Manual setting by image experts is the traditional way of hyperparameter tuning, which is time-consuming and biased towards human perception. Recently, ISP has been optimized by the feedback of the downstream tasks based on different optimization algorithms. Unfortunately, these methods should keep parameters fixed during the inference stage for arbitrary input without considering that each image should have specific parameters based on its feature. To this end, we propose an attention-aware learning method that integrates the parameter prediction network into ISP tuning and utilizes the multi-attention mechanism to generate the attentive mapping between the input RAW image and the parameter space. The proposed method integrates downstream tasks end-to-end, predicting specific parameters for each image. We validate the proposed method on object detection, image segmentation, and human viewing tasks.