Title: Efficient Spatially-Variant Convolution via Differentiable Sparse Kernel Complex
Primary area: optimization
Keywords: Kernel Approximation, Differentiable Filtering, Spatially-Varying Convolution, Efficient Image Processing

Abstract: Image convolution with complex kernels is a fundamental operation in photography, scientific imaging, and animation effects, yet direct dense convolution is computationally prohibitive on resource-limited devices. 
Existing approximations, such as simulated annealing or low-rank decompositions, either lack efficiency or fail to capture non-convex kernels.
We introduce a differentiable kernel decomposition framework that represents a target spatially-variant, dense, complex kernel using a set of sparse kernel samples. Our approach features (i) a decomposition that enables differentiable optimization of sparse kernels, (ii) a dedicated initialization strategy for non-convex shapes to avoid poor local minima, and (iii) a kernel-space interpolation scheme that extends single-kernel filtering to spatially varying filtering without retraining and additional runtime overhead.
Experiments on Gaussian and non-convex kernels show that our method achieves higher fidelity than simulated annealing and significantly lower cost than low-rank decompositions. Our approach provides a practical solution for mobile imaging and real-time rendering, while remaining fully differentiable for integration into broader learning pipelines.

TL;DR: This work introduces a differentiable framework for decomposing complex kernels into optimized sparse layers, enabling high-performance, spatially-varying filtering via a filter-space interpolation scheme.