Light Field Reconstruction Via Deep Adaptive Fusion of Hybrid Lenses

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)12050-12067
Number of pages18
Journal / PublicationIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume45
Issue number10
Online published20 Jun 2023
Publication statusPublished - Oct 2023

Abstract

This paper explores the problem of reconstructing high-resolution light field (LF) images from hybrid lenses, including a high-resolution camera surrounded by multiple low-resolution cameras. The performance of existing methods is still limited, as they produce either blurry results on plain textured areas or distortions around depth discontinuous boundaries. To tackle this challenge, we propose a novel end-to-end learning-based approach, which can comprehensively utilize the specific characteristics of the input from two complementary and parallel perspectives. Specifically, one module regresses a spatially consistent intermediate estimation by learning a deep multidimensional and cross-domain feature representation, while the other module warps another intermediate estimation, which maintains the high-frequency textures, by propagating the information of the high-resolution view. We finally leverage the advantages of the two intermediate estimations adaptively via the learned confidence maps, leading to the final high-resolution LF image with satisfactory results on both plain textured areas and depth discontinuous boundaries. Besides, to promote the effectiveness of our method trained with simulated hybrid data on real hybrid data captured by a hybrid LF imaging system, we carefully design the network architecture and the training strategy. Extensive experiments on both real and simulated hybrid data demonstrate the significant superiority of our approach over state-of-the-art ones. To the best of our knowledge, this is the first end-to-end deep learning method for LF reconstruction from a real hybrid input. We believe our framework could potentially decrease the cost of high-resolution LF data acquisition and benefit LF data storage and transmission. The code will be publicly available at https://github.com/jingjin25/LFhybridSR-Fusion. © 2023 IEEE.

Research Area(s)

  • Light field, super-resolution, hybrid imaging system, deep learning, fusion, depth