Producing　a　high　dynamic　range　(HDR)　image　from　two　low　dynamic　range　(LDR)　images　with　extreme　exposures　is　challenging　due　to　the　lack　of　well-exposed　contents.　Existing　works　either　use　pixel　fusion　based　on　weighted　quantization　or　conduct　feature　fusion　using　deep　learning　techniques.　In　contrast　to　these　methods,　our　core　idea　is　to　progressively　incorporate　the　pixel　domain　knowledge　of　LDR　images　into　the　feature　fusion　process.　Specifically,　we　propose　a　novel　Prior-Aware　Generative　Adversarial　Network　(PA-GAN),　along　with　a　new　dual-level　loss　for　two　exposure　fusion.　The　proposed　PA-GAN　is　composed　of　a　content-prior-guided　encoder　and　a　detail-prior-guided　decoder,　respectively　in　charge　of　content　fusion　and　detail　calibration.　We　further　train　the　network　using　a　dual-level　loss　that　combines　the　semantic-level　loss　and　pixel-level　loss.　Extensive　qualitative　and　quantitative　evaluations　on　diverse　image　datasets　demonstrate　that　our　proposed　PA-GAN　has　superior　performance　than　state-of-the-art　methods.