Images　captured　in　low-light　scenes　are　susceptible　to　multiple　degradations　such　as　darkness,　noise,　and　blur,　resulting　in　poor　visibility　and　visual　perception.　Multi-degraded　low-light　image　enhancement　poses　challenges　to　existing　image　enhancement　methods　as　follows:　on　the　one　hand,　low-light　image　enhancement　or　deblurring　methods　cannot　handle　all　three　types　of　degradation,　and　the　effect　of　the　combination　strategy　is　limited　by　the　increased　computational　cost　and　error　accumulation.　On　the　other　hand,　the　existing　multi-degraded　low-light　image　enhancement　method　adopts　the　strategy　of　enhancing　brightness　first　and　then　removing　blur,　and　this　sequential　processing　manner　increases　the　risk　of　losing　feature　cues　and　is　not　conducive　to　detail　recovery.　To　cope　with　the　above　challenges,　this　paper　proposes　the　progressive　edge-aware　interactive　enhancement　network　(PEIE-Net),　which　reduces　the　loss　of　feature　details　by　designing　a　step-by-step　enhancement　process.　Specifically,　our　network　consists　of　an　image　enhancement　branch　and　an　edge　information　prediction　branch.　In　each　enhancement　stage　of　the　image　enhancement　branch,　a　self-attention　modulation　prediction　module　is　designed　to　extract　the　global　information,　which　is　used　for　adaptive　modulation　in　the　channel　modulation　module　and　multi-scale　restoration　module.　In　the　edge　information　prediction　branch,　the　spatial-frequency　domain　feature　transformation　module　is　developed　to　extract　the　edge　perceptual　information.　The　edge　perceptual　information　is　used　to　predict　the　edges　of　high-quality　images　while　also　fused　with　the　image　enhancement　features,　simulating　the　interaction　between　different　perceptions　within　the　human　visual　system.　In　addition,　we　propose　scene　brightness　estimation　loss　to　coordinate　the　multiple　progressive　enhancement　stages.　Experiments　on　synthetic　and　real　datasets　demonstrate　the　effectiveness　and　sophistication　of　our　method　for　enhancing　low-light,　noisy,　and　blur-degraded　images,　and　can　be　used　for　low-light　image　enhancement　and　super-resolution　tasks.　©　2025　Chinese　Institute　of　Electronics.　All　rights　reserved.