DCT based Fusion of Variable Exposure Images for HDRI

TL;DR

This paper introduces a DCT-based method for fusing multiple exposure images to create high dynamic range images, emphasizing transform domain averaging to improve image quality in computational photography.

Contribution

It presents a novel DCT-based fusion technique that avoids pyramidal processing and uses transform domain averaging to effectively combine exposure images.

Findings

DCT coefficients correlate with well-exposedness, contrast, and saturation.

The method achieves effective image fusion without pyramidal processing.

Transform domain averaging simplifies the fusion process.

Abstract

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 Transform (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.