CoordFill: Efficient High-Resolution Image Inpainting via Parameterized Coordinate Querying (2303.08524v1)

Abstract: Image inpainting aims to fill the missing hole of the input. It is hard to solve this task efficiently when facing high-resolution images due to two reasons: (1) Large reception field needs to be handled for high-resolution image inpainting. (2) The general encoder and decoder network synthesizes many background pixels synchronously due to the form of the image matrix. In this paper, we try to break the above limitations for the first time thanks to the recent development of continuous implicit representation. In detail, we down-sample and encode the degraded image to produce the spatial-adaptive parameters for each spatial patch via an attentional Fast Fourier Convolution(FFC)-based parameter generation network. Then, we take these parameters as the weights and biases of a series of multi-layer perceptron(MLP), where the input is the encoded continuous coordinates and the output is the synthesized color value. Thanks to the proposed structure, we only encode the high-resolution image in a relatively low resolution for larger reception field capturing. Then, the continuous position encoding will be helpful to synthesize the photo-realistic high-frequency textures by re-sampling the coordinate in a higher resolution. Also, our framework enables us to query the coordinates of missing pixels only in parallel, yielding a more efficient solution than the previous methods. Experiments show that the proposed method achieves real-time performance on the 2048$\times$2048 images using a single GTX 2080 Ti GPU and can handle 4096$\times$4096 images, with much better performance than existing state-of-the-art methods visually and numerically. The code is available at: https://github.com/NiFangBaAGe/CoordFill.

• The paper introduces CoordFill, which leverages parameterized coordinate querying to selectively inpaint missing high-resolution regions with enhanced efficiency.
• It employs a two-stage approach combining an attentional FFC-based parameter generator with MLP-based pixel querying to capture both global and local image features.
• Experiments demonstrate that CoordFill achieves real-time performance and superior quality metrics on datasets like CelebA-HQ and Places2 compared to state-of-the-art methods.

Examining CoordFill: An Efficient Approach for High-Resolution Image Inpainting

The paper presents CoordFill, a novel framework designed to address the computational challenges of high-resolution image inpainting. The proposed method leverages the advantages of parameterized coordinate querying, capitalizing on continuous implicit representations to efficiently generate photo-realistic completed images, particularly focusing on only synthesizing the missing pixels instead of the entire image. This approach allows the method to operate significantly faster than traditional convolutional neural network (CNN) based techniques, improving both performance and efficiency.

Overview and Methodology

CoordFill employs a two-stage process: the parameter generation network and the pixel-wise querying network.

1. Parameter Generation Network: This network is tasked with producing spatial-adaptive parameters that correspond to the image input, which is initially downsampled. These parameters are generated using an attentional Fast Fourier Convolution (FFC)-based network, which captures both global and local features by incorporating attentional mechanisms to better focus on the masked regions.
2. Pixel-Wise Querying Network: The subsequent step involves using the generated parameters as inputs for synthesizing pixel values through Multi-Layer Perceptrons (MLPs). The coordinates of only the missing pixels are queried and processed, which significantly reduces the computational overhead compared to traditional fully image-based synthesis methods.

The novelty of CoordFill lies in its meta-learning strategy, where it employs a spatial-adaptive parameter framework that allows for efficient handling of high-resolution images by reducing unnecessary computations focused on non-masked regions.

Experimental Evaluation

CoordFill was experimentally evaluated on several datasets, including Places2, Unsplash, and CelebA-HQ, across various image resolutions ranging up to 4096x4096. The method demonstrated superior performance reflected in metrics such as PSNR, SSIM, and LPIPS, particularly in scenarios where high-resolution images are involved. Noteworthy findings include the method's capability to achieve real-time performance with a single GTX 2080 Ti GPU, marking a substantial efficiency improvement over state-of-the-art methods like LaMa and ZITS. Additionally, CoordFill managed to handle substantially higher resolutions without encountering memory limitations that typically constrain existing methodologies.

Implications and Future Prospects

The implications of this research signify considerable advancements in the practical deployment of image inpainting technologies, particularly for applications demanding high-realism texture synthesis in high-resolution images. The strategic focus on coordinate-specific querying heralds a shift from exhaustive image synthesis toward more computationally prudent approaches, potentially paving the way for future developments in real-time image editing and enhancement tasks across various digital platforms.

Looking ahead, further exploration into enhancing the cross-domain adaptability of this framework could extend its application to other challenging domains within AI-powered image processing, such as video frame interpolation and 3D scene reconstruction. Moreover, addressing current limitations, such as maintaining high-frequency texture details and managing occlusions in complex scenes, would bolster the robustness and versatility of CoordFill.

In conclusion, CoordFill presents a promising direction in efficient image inpainting, achieving a balance between computational efficiency and high-quality output, establishing itself as a pioneering approach in the domain of high-resolution image synthesis.