Quality Enhancement via Spatial-Angular Deformable Convolution for Compressed Light Field

To address the complexity of data in high-dimensional light field (LF), LF compression has emerged as a prominent research focus in recent years. However, diverse compression methods inevitably entail the loss of scene information, leading to notable distortions. To rectify this issue and recover lost information during the LF compression process, this paper introduces an innovative algorithm for enhancing the quality of compressed LF images. The proposed method addresses LF quality enhancement in two stages for any given LF viewpoint. In the initial stage, we leverage both local and global viewpoints for shallow feature extraction. Local viewpoints integrate spatial-angular contextual information through deformable convolution networks, while global viewpoints serve as auxiliary features to preserve fine details in the LF. In the subsequent stage, a dense residual network is employed for fine quality enhancement, resulting in improved LF viewpoints. Numerous experiments affirm that our approach has attained state-of-the-art performance, significantly enhancing the quality of compressed LF images.