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Deep learning based image denoising methods have been extensivelyinvestigated. In this paper, attention mechanism enhanced kernel predictionnetworks (AME-KPNs) are proposed for burst image denoising, in which, nearlycost-free attention modules are adopted to first refine the feature maps and tofurther make a full use of the inter-frame and intra-frame redundancies withinthe whole image burst. The proposed AME-KPNs output per-pixelspatially-adaptive kernels, residual maps and corresponding weight maps, inwhich, the predicted kernels roughly restore clean pixels at theircorresponding locations via an adaptive convolution operation, andsubsequently, residuals are weighted and summed to compensate the limitedreceptive field of predicted kernels. Simulations and real-world experimentsare conducted to illustrate the robustness of the proposed AME-KPNs in burstimage denoising.

Attention Mechanism Enhanced Kernel Prediction Networks for Denoising of Burst Images