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  • 标题:DCT based Fusion of Variable Exposure Images for HDRI
  • 本地全文:下载
  • 作者:Vivek Ramakrishnan ; D.J.Pete
  • 期刊名称:Computer Science & Information Technology
  • 电子版ISSN:2231-5403
  • 出版年度:2021
  • 卷号:11
  • 期号:15
  • 语种:English
  • 出版社:Academy & Industry Research Collaboration Center (AIRCC)
  • 摘要:Combining images with different exposure settings are of prime importance in the field of computational photography. Both transform domain approach and filtering based approaches are possible for fusing multiple exposure images, to obtain the well-exposed image. We propose a Discrete Cosine Trans- form (DCT-based) approach for fusing multiple exposure images. The input image stack is processed in the transform domain by an averaging operation and the inverse transform is performed on the averaged image obtained to generate the fusion of multiple exposure image. The experimental observation leads us to the conjecture that the obtained DCT coefficients are indicators of parameters to measure well-exposedness, contrast and saturation as specified in the traditional exposure fusion based approach and the averaging performed indicates equal weights assigned to the DCT coefficients in this non- parametric and non pyramidal approach to fuse the multiple exposure stack.
  • 关键词:Discrete;Exposure;Cosine;Fusion;Coefficients;Transform;Contrast;Saturation;Weights
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