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This paper introduces a novel image representation based on the sparse Laplacian field, leveraging the mathematical guarantee of unique image reconstruction from its Laplacian via solving a Poisson equation. To efficiently solve this Poisson equation, they propose a shared-kernel wavelet neural network with fewer than 200 parameters. The resulting method achieves real-time image reconstruction with higher accuracy compared to existing techniques, while maintaining linear computational complexity.
Achieve real-time, accurate image reconstruction from sparse Laplacian fields using a wavelet neural network with only 200 parameters.
The Laplacian operator transforms the image into its Laplacian field, which usually is sparse and satisfies a stable distribution. On the other hand, an image can be uniquely reconstructed from its Laplacian field via solving a Poisson equation with a proper boundary condition. Such uniqueness is mathematically guaranteed. Thanks to these properties, we propose to use the sparse Laplacian field to present the image. We first show that the Laplacian field is sparse and satisfies a stable distribution on hundreds images. Then, we show that the image can be accurately reconstruct from its Laplacian field. For the reconstruction task, we propose a shared-kernel wavelet neural network, which solves the Poisson equation and has three advantages. First, it has less than {\bf 0.0002M} parameters, which is compact enough for most of devices. Second, it has linear computation complexity, leading to a real-time reconstruction. Third, it achieves higher accuracy than previous methods. Several numerical experiments are conducted to show the effectiveness and efficiency of the sparse Laplacian field and the proposed Poisson solver. The proposed method can be applied in a large range of applications such as image compression, low light enhancement, object tracking, etc.
