Sharp-NeRF: Grid-based Fast Deblurring Neural Radiance Fields Using
  Sharpness Prior
Byeonghyeon Lee Howoong Lee Usman Ali Eunbyung Park
Neural Radiance Fields (NeRF) have shown remarkable performance in neural
rendering-based novel view synthesis. However, NeRF suffers from severe visual
quality degradation when the input images have been captured under imperfect
conditions, such as poor illumination, defocus blurring, and lens aberrations.
Especially, defocus blur is quite common in the images when they are normally
captured using cameras. Although few recent studies have proposed to render
sharp images of considerably high-quality, yet they still face many key
challenges. In particular, those methods have employed a Multi-Layer Perceptron
(MLP) based NeRF, which requires tremendous computational time. To overcome
these shortcomings, this paper proposes a novel technique Sharp-NeRF -- a
grid-based NeRF that renders clean and sharp images from the input blurry
images within half an hour of training. To do so, we used several grid-based
kernels to accurately model the sharpness/blurriness of the scene. The
sharpness level of the pixels is computed to learn the spatially varying blur
kernels. We have conducted experiments on the benchmarks consisting of blurry
images and have evaluated full-reference and non-reference metrics. The
qualitative and quantitative results have revealed that our approach renders
the sharp novel views with vivid colors and fine details, and it has
considerably faster training time than the previous works. Our project page is
available at https://benhenryl.github.io/SharpNeRF/