Exploring Progressive Hybrid-degraded Image Processing for Homography Estimation

Recent studies have shown that machine models do not coincide with the human perception of image quality, making mainstream image enhancement methods not always compatible with downstream tasks. To ameliorate this issue, this paper targets homography estimation, which is a fundamental step in image interpretation, to explore a hybrid-degraded image enhancement approach. Specifically, we build a reinforcement learning image processing framework with multiple lightweight tools, and develop a two-policy agent to progressively handle hybrid-degraded images. To match homography estimators with different properties, we further propose an environmental epistemic model (EEM) to build task-specific prior knowledge of uncertain environments. During the training process, the EEM is updated online and used to guide the agent's exploration and exploitation. Comprehensive experiments are conducted on the aerial dataset where images are degraded from four visual perspectives: brightness, contrast, sharpness and stimulus. Results show that our agent can be trained to generate high-quality images for both learning-based and traditional homography estimators.