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 this https URL

提出了一种名为 Sharp-NeRF 的基于网格的 NeRF 技术，可以在半小时的训练时间内从模糊的图像中生成干净和清晰的图像，通过精确建模场景的清晰度 / 模糊度，并计算像素的清晰度水平以学习空间变化的模糊核，实验证明与之前的方法相比，具有更快的训练时间，并能生成色彩鲜艳、细节丰富的清晰新视图。