HDRfeat: A Feature-Rich Network for High Dynamic Range Image Reconstruction

Autor:	Zhu, Lingkai, Zhou, Fei, Liu, Bozhi, Göksel, Orcun
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	A major challenge for high dynamic range (HDR) image reconstruction from multi-exposed low dynamic range (LDR) images, especially with dynamic scenes, is the extraction and merging of relevant contextual features in order to suppress any ghosting and blurring artifacts from moving objects. To tackle this, in this work we propose a novel network for HDR reconstruction with deep and rich feature extraction layers, including residual attention blocks with sequential channel and spatial attention. For the compression of the rich-features to the HDR domain, a residual feature distillation block (RFDB) based architecture is adopted. In contrast to earlier deep-learning methods for HDR, the above contributions shift focus from merging/compression to feature extraction, the added value of which we demonstrate with ablation experiments. We present qualitative and quantitative comparisons on a public benchmark dataset, showing that our proposed method outperforms the state-of-the-art. Comment: 4 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.04238 Zobrazit plný text záznamu View this record from Arxiv