  Deep learning based image denoising methods have been extensively
investigated. In this paper, attention mechanism enhanced kernel prediction
networks (AME-KPNs) are proposed for burst image denoising, in which, nearly
cost-free attention modules are adopted to first refine the feature maps and to
further make a full use of the inter-frame and intra-frame redundancies within
the whole image burst. The proposed AME-KPNs output per-pixel
spatially-adaptive kernels, residual maps and corresponding weight maps, in
which, the predicted kernels roughly restore clean pixels at their
corresponding locations via an adaptive convolution operation, and
subsequently, residuals are weighted and summed to compensate the limited
receptive field of predicted kernels. Simulations and real-world experiments
are conducted to illustrate the robustness of the proposed AME-KPNs in burst
image denoising.
