High　Dynamic　Range　Imaging　(HDRI)　is　a　technology　of　fusing　multiple　Low　Dynamic　Range　(LDR)　images　to　extend　image　dynamic　range,　restore　image　contents　and　generate　high　dynamic　range　(HDR)　images.　It　provides　a　practical　solution　to　the　problem　of　content　loss　in　captured　images　due　to　the　limited　dynamic　range　of　the　camera　sensors.　With　decades　of　studies,　numerous　promising　approaches　have　been　proposed　and　near-optimal　performance　has　been　achieved　for　the　HDRI　static　scenes　with　no　object　motions　and　well-exposed　contents.　However,　object　motions　or　camera　shifts　are　inevitable　in　practical　scenarios.　Directly　using　traditional　HDRI　methods　would　induce　severe　ghosting　artifacts　into　the　merged　HDR　image.　This　makes　HDRI　with　simple　merging　process　inapplicable　in　real-world　applications,　which　poses　a　challenge　to　the　HDRI　task.　Thus,　the　study　on　HDRI　of　dynamic　scenes　has　grown　rapidly.　Recent　advances　focus　on　exploring　the　power　of　deep　convolutional　neural　networks　(CNNs)　to　achieve　a　better　performance.　Among　these　CNN-based　methods,　the　feature　aggregation　plays　a　crucial　role　in　completing　image　contents　and　eliminating　ghosting　artifacts.　Equipped　with　skip　connections　or　attention　modules,　the　features　derived　from　multiple　LDR　images　are　first　concatenated　and　then　gradually　focus　on　different　local　aspects　via　stacked　convolutions.　However,　such　aggregation　schemes　generally　neglect　to　utilize　the　rich　contextual　dependencies　across　LDR　image　sequence,　the　textural　coherence　among　features　have　not　been　fully　exploited.　To　address　this　issue,　this　paper　proposes　a　novel　Coherence-Aware　Feature　Aggregation　(CAFA)　scheme　that　samples　grids　with　the　same　contextual　information　instead　of　the　same　position　across　input　features　during　convolutional　operations,　so　that　contextual　coherence　can　be　explicitly　incorporated　into　feature　aggregation.　Based　on　CAFA,　this　paper　further　proposes　Coherence-Aware　HDR　Network　(CAHDRNet)　for　HDRI　of　dynamic　scenes.　To　facilitate　the　incorporation　of　CAFA,　the　proposed　CAHDRNet　is　constructed　by　designing　three　additional　learnable　modules.　Firstly,　a　learnable　feature　extractor,　which　is　built　upon　a　VGG-19　pre-trained　on　ImageNet,　is　used　to　extract　features　from　each　LDR　image.　The　parameters　will　be　updated　during　end-to-end　training.　Such　a　design　enables　a　joint　feature　learning　of　LDR　images　which　creates　a　solid　foundation　for　applying　the　coherence　evaluation　in　CAFA.　Then,　the　proposed　CAFA　module　is　applied　to　aggregate　the　features　by　sampling　grids　with　the　same　contextual　information　in　each　image　features.　Next,　a　Multi-Scale　Residual　Hallucinating　(MSRH)　module　is　proposed　to　process　the　aggregated　features,　in　which　the　features　are　learnt　across　different　scales　of　dilated　rates　to　achieve　a　more　powerful　feature　representation　and　hallucinate　plausible　details　in　the　missing　regions.　Also,　a　soft　attention　module　is　equipped　to　learn　the　importance　of　different　image　regions　for　obtaining　the　features　that　are　complementary　to　the　reference　image　during　skip　connection.　Various　experiments　are　conducted　to　validate　the　effectiveness　of　our　proposed　CAHDRNet,　where　it　demonstrates　superior　performance　over　state-of-the-art　(SOTA)　methods.　Specifically,　the　proposed　CAHDRNet　improves　the　HDR-VDP-2　and　PSNR-h　values　on　Kalantari＇s　dataset　over　the　second-best　AHDRNet　by　1.　61　and　0.　68,　respectively.　©　2024　Science　Press.　All　rights　reserved.