KNN Local Attention for Image Restoration

Recent works attempt to integrate the non-local operation with CNNs or Transformer, achieving remarkable performance in image restoration tasks. The global similarity, however, has the problems of the lack of locality and the high computational complexity that is quadratic to an input resolution. The local attention mechanism alleviates these issues by introducing the inductive bias of the locality with convolution-like operators. However, by focusing only on adjacent positions, the local attention suffers from an insufficient receptive field for image restoration. In this paper, we propose a new attention mechanism for image restoration, called k-NN Image Transformer (KiT), that rectifies above mentioned limitations. Specifically, the KiT groups k-nearest neighbor patches with locality sensitive hashing (LSH), and the grouped patches are aggregated into each query patch by performing a pair-wise local attention. In this way, the pair-wise operation establishes non-local connectivity while maintaining the desired properties of the local attention, i.e., inductive bias of locality and linear complexity to input resolution. The proposed method outperforms state-of-the-art restoration approaches on image denoising, deblurring and deraining benchmarks. The code will be available at https://sites.google.com/view/cvpr22-kit.