An image-guided network for depth edge enhancement

Kuan Ting Lee, En Rwei Liu, Jar Ferr Yang, Li Hong

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


With the rapid development of 3D coding and display technologies, numerous applications are emerging to target human immersive entertainments. To achieve a prime 3D visual experience, high accuracy depth maps play a crucial role. However, depth maps retrieved from most devices still suffer inaccuracies at object boundaries. Therefore, a depth enhancement system is usually needed to correct the error. Recent developments by applying deep learning to deep enhancement have shown their promising improvement. In this paper, we propose a deep depth enhancement network system that effectively corrects the inaccurate depth using color images as a guide. The proposed network contains both depth and image branches, where we combine a new set of features from the image branch with those from the depth branch. Experimental results show that the proposed system achieves a better depth correction performance than state of the art advanced networks. The ablation study reveals that the proposed loss functions in use of image information can enhance depth map accuracy effectively.

Original languageEnglish
Article number6
JournalEurasip Journal on Image and Video Processing
Issue number1
Publication statusPublished - 2022 Dec

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Electrical and Electronic Engineering


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