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In recent years, image blending has gained popularity for its ability tocreate visually stunning content. However, the current image blendingalgorithms mainly have the following problems: manually creating image blendingmasks requires a lot of manpower and material resources; image blendingalgorithms cannot effectively solve the problems of brightness distortion andlow resolution. To this end, we propose a new image blending method withautomatic mask generation: it combines semantic object detection andsegmentation with mask generation to achieve deep blended images based on ourproposed new saturation loss and two-stage iteration of the PAN algorithm tofix brightness distortion and low-resolution issues. Results on publiclyavailable datasets show that our method outperforms other classical imageblending algorithms on various performance metrics, including PSNR and SSIM.

Image Blending Algorithm with Automatic Mask Generation