… Given a set of severe motion blurred images, our bundle adjusted deblur NeRF (BAD-NeRF)
jointly learns the neural … It synthesizes novel images of higher quality than prior works. …

… from blurry input. We model the blurring process by convolving a clean image using a blur
kernel … A novel deformable sparse kernel (DSK) module is proposed to model the blur kernel …

… recovery in image processing. In this article, an ensemble convolution neural network (CNN)
is … and classify four types of blur images: defocus blur, Gaussian blur, haze blur, and motion …

… Besides, our method could easily adjust the blur effects of different exposure time during the
image capturing in the real world, by adaptively tuning translations of object and background…

… dataset in which blurred images have been classified using … neural networks are trained
by minimizing empirical risk for a finite number of image pairs, and parameters are adjusted to …

… For generating blur map, we propose a six-layer convolutional neural network, named
BCNet. … . However, one can adaptively adjust the weights by observing the quality of the blur …

… , and blur. In this work, we propose an Attention-guided Progressive Neural Texture Fusion
(… iris and brain are able to constantly adapt and adjust the responses to such strong stimulant …

… is also an important tool in cinematography, where filmmakers will carefully adjust the …
In Section 3 we define a deep neural network architecture based on our line prediction layer…

… image noise, image blur, image contrast, compression artifacts, etc. on the performance of
deep neural networks on image … adaptively adjusted regularization along with image size …

… been practiced by blurring the images at earlier … Images as neural networks. An alternative
formulation of the problem is to learn a coordinate-based image representation with a neural …