The　image　registration　and　fusion　process　of　image　stitching　algorithms　entails　significant　computational　costs,　and　the　use　of　robust　stitching　algorithms　with　good　performance　is　limited　in　real-time　applications　on　PCs　(personal　computers)　and　embedded　systems.　Fast　image　registration　and　fusion　algorithms　suffer　from　problems　such　as　ghosting　and　dashed　lines,　resulting　in　suboptimal　display　effects　on　the　stitching.　Consequently,　this　study　proposes　a　multi-channel　image　stitching　approach　based　on　fast　image　registration　and　fusion　algorithms,　which　enhances　the　stitching　effect　on　the　basis　of　fast　algorithms,　thereby　augmenting　its　potential　for　deployment　in　real-time　applications.　First,　in　the　image　registration　stage,　the　gridded　Binary　Robust　Invariant　Scalable　Keypoints　(BRISK)　method　was　used　to　improve　the　matching　efficiency　of　feature　points,　and　the　Grid-based　Motion　Statistics　(GMS)　algorithm　with　a　bidirectional　rough　matching　method　was　used　to　improve　the　matching　accuracy　of　feature　points.　Then,　the　optimal　seam　algorithm　was　used　in　the　image　fusion　stage　to　obtain　the　seam　line　and　construct　the　fusion　area.　The　seam　and　transition　areas　were　fused　using　the　fade-in　and　fade-out　weighting　algorithm　to　obtain　smooth　and　high-quality　stitched　images.　The　experimental　results　demonstrate　the　performance　of　our　proposed　method　through　an　improvement　in　image　registration　and　fusion　metrics.　We　compared　our　approach　with　both　the　original　algorithm　and　other　existing　methods　and　achieved　significant　improvements　in　eliminating　stitching　artifacts　such　as　ghosting　and　discontinuities　while　maintaining　the　efficiency　of　fast　algorithms.