  In low light or short-exposure photography the image is often corrupted by
noise. While longer exposure helps reduce the noise, it can produce blurry
results due to the object and camera motion. The reconstruction of a noise-less
image is an ill posed problem. Recent approaches for image denoising aim to
predict kernels which are convolved with a set of successively taken images
(burst) to obtain a clear image. We propose a deep neural network based
approach called Multi-Kernel Prediction Networks (MKPN) for burst image
denoising. MKPN predicts kernels of not just one size but of varying sizes and
performs fusion of these different kernels resulting in one kernel per pixel.
The advantages of our method are two fold: (a) the different sized kernels help
in extracting different information from the image which results in better
reconstruction and (b) kernel fusion assures retaining of the extracted
information while maintaining computational efficiency. Experimental results
reveal that MKPN outperforms state-of-the-art on our synthetic datasets with
different noise levels.
