END-TO-END LEARNED LIGHT FIELD IMAGE RESCALING USING JOINT SPATIAL-ANGULAR AND EPIPOLAR INFORMATION

Light field (LF) rescaling is indispensable in accommodating different LF image resolutions for different applications. Unlikely most recent studies which only execute learned LF upscaling from a predefined downscaling method, we propose a novel LF rescaling framework by jointly optimizing learned LF downscaling and upscaling as a combined task. Specifically, our light field rescaling network (LFRN) simultaneously extracts features from different 2D subspaces of LF data (e.g., spatial-angular and epipolar subspaces) to fully handle 4D LF image information. Our newly designed attention fusion module (AFM) adaptively combines these two data features based on learnable embedding weights. Due to joint optimization of the learned LF downscaling and upscaling tasks, our LFRN method can achieve significant performance gain in both objective and subjective visual qualities compared to conventional predefined downscaling with learned LF upscaling task.