Texture and exposure awareness based refill for HDRI reconstruction of saturated and occluded areas

Jianming Zhou, Yipeng Deng, Qin Liu, Takeshi Ikenaga

Research output: Contribution to journalArticlepeer-review

Abstract

High-dynamic-range image (HDRI) displays scenes as vivid as the real scenes. HDRI can be reconstructed by fusing a set of bracketed-exposure low-dynamic-range images (LDRI). For the reconstruction, many works succeed in removing the ghost artefacts caused by moving objects. The critical issue is reconstructing the areas which are saturated due to bad exposure and occluded due to motion with no ghost artefacts. To overcome this issue, this paper proposes texture and exposure awareness based refill. The proposed work first locates the saturated and occluded areas existing in input image set, then refills background textures or patches containing rough exposure and colour information into located areas. Proposed work can be integrated with multiple existing ghost removal works to improve the reconstruction result. Experimental results show that proposed work removes the ghost artefacts caused by saturated and occluded areas in subjective evaluation. For the objective evaluation, the proposed work improves the HDR-VDP-2 evaluation result for multiple conventional works by 1.33% on average.

Original languageEnglish
JournalIET Image Processing
DOIs
Publication statusAccepted/In press - 2021

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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