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Image outpainting is a very intriguing problem as the outside of a givenimage can be continuously filled by considering as the context of the image.This task has two main challenges. The first is to maintain the spatialconsistency in contents of generated regions and the original input. The secondis to generate a high-quality large image with a small amount of adjacentinformation. Conventional image outpainting methods generate inconsistent,blurry, and repeated pixels. To alleviate the difficulty of an outpaintingproblem, we propose a novel image outpainting method using bidirectionalboundary region rearrangement. We rearrange the image to benefit from the imageinpainting task by reflecting more directional information. The bidirectionalboundary region rearrangement enables the generation of the missing regionusing bidirectional information similar to that of the image inpainting task,thereby generating the higher quality than the conventional methods usingunidirectional information. Moreover, we use the edge map generator thatconsiders images as original input with structural information and hallucinatesthe edges of unknown regions to generate the image. Our proposed method iscompared with other state-of-the-art outpainting and inpainting methods bothqualitatively and quantitatively. We further compared and evaluated them usingBRISQUE, one of the No-Reference image quality assessment (IQA) metrics, toevaluate the naturalness of the output. The experimental results demonstratethat our method outperforms other methods and generates new images with360{\deg}panoramic characteristics.

Painting Outside as Inside: Edge Guided Image Outpainting via Bidirectional Rearrangement with Progressive Step Learning