Image Inpainting via Correlated Multi-resolution Feature Projection.

With the advancement in image editing applications, image inpainting is gaining more attention due to its ability to recover corrupted images efficiently. Also, the existing methods for image inpainting either use two-stage coarse-to-fine architectures or single-stage architectures with a deeper network. On the other hand, shallow network architectures lack the quality of results and the methods with remarkable inpainting quality have high complexity in terms of number of parameters or average run time. Despite the improvement in the inpainting quality, these methods still lack the correlated local and global information. In this work, we propose a single-stage multi-resolution generator architecture for image inpainting with moderate complexity and superior outcomes. Here, a multi-kernel non-local (MKNL) attention block is proposed to merge the feature maps from all the resolutions. Further, a feature projection block is proposed to project features of MKNL to respective decoder for effective reconstruction of image. Also, a valid feature fusion block is proposed to merge encoder skip connection features at valid region and respective decoder features at hole region. This ensures that there will not be any redundant feature merging while reconstruction of image. Effectiveness of the proposed architecture is verified on CelebA-HQ [1], [2] and Places2 [3] datasets corrupted with publicly available NVIDIA mask dataset [4]. The detailed ablation study, extensive result analysis, and application of object removal prove the robustness of the proposed method over existing state-of-the-art methods for image inpainting.

Pseudo Decoder Guided Light-Weight Architecture for Image Inpainting.

Image inpainting is one of the most important and widely used approaches where input image is synthesized at the missing regions. This has various applications like undesired object removal, virtual garment shopping, etc. The methods used for image inpainting may use the knowledge of hole locations to effectively regenerate contents in an image. Existing image inpainting methods give astonishing results with coarse-to-fine architectures or with use of guided information like edges, structures, etc. The coarse-to-fine architectures require umpteen resources leading to high computation cost of the architecture. Other methods with edge or structural information depend on the available models to generate guiding information for inpainting. In this context, we have proposed computationally efficient, light-weight network for image inpainting with very less number of parameters (0.97M) and without any guided information. The proposed architecture consists of the multi-encoder level feature fusion module, pseudo decoder and regeneration decoder. The encoder multi level feature fusion module extracts relevant information from each of the encoder levels to merge structural and textural information from various receptive fields. This information is then processed with pseudo decoder followed by space depth correlation module to assist regeneration decoder for inpainting task. The experiments are performed with different types of masks and compared with the state-of-the-art methods on three benchmark datasets i.e., Paris Street View (PARIS_SV), Places2 and CelebA_HQ. Along with this, the proposed network is tested on high resolution images ( 1024×1024 and 2048 ×2048 ) and compared with the existing methods. The extensive comparison with state-of-the-art methods, computational complexity analysis, and ablation study prove the effectiveness of the proposed framework for image inpainting.