30 research outputs found
Convolutional Dictionary Regularizers for Tomographic Inversion
There has been a growing interest in the use of data-driven regularizers to
solve inverse problems associated with computational imaging systems. The
convolutional sparse representation model has recently gained attention, driven
by the development of fast algorithms for solving the dictionary learning and
sparse coding problems for sufficiently large images and data sets.
Nevertheless, this model has seen very limited application to tomographic
reconstruction problems. In this paper, we present a model-based tomographic
reconstruction algorithm using a learnt convolutional dictionary as a
regularizer. The key contribution is the use of a data-dependent weighting
scheme for the l1 regularization to construct an effective denoising method
that is integrated into the inversion using the Plug-and-Play reconstruction
framework. Using simulated data sets we demonstrate that our approach can
improve performance over traditional regularizers based on a Markov random
field model and a patch-based sparse representation model for sparse and
limited-view tomographic data sets
Convolutional Sparse Representations with Gradient Penalties
While convolutional sparse representations enjoy a number of useful
properties, they have received limited attention for image reconstruction
problems. The present paper compares the performance of block-based and
convolutional sparse representations in the removal of Gaussian white noise.
While the usual formulation of the convolutional sparse coding problem is
slightly inferior to the block-based representations in this problem, the
performance of the convolutional form can be boosted beyond that of the
block-based form by the inclusion of suitable penalties on the gradients of the
coefficient maps
Image Enhancement in Foggy Images using Dark Channel Prior and Guided Filter
Haze is very apparent in images shot during periods of bad weather (fog). The image's clarity and readability are both diminished as a result. As part of this work, we suggest a method for improving the quality of the hazy image and for identifying any objects hidden inside it. To address this, we use the picture enhancement techniques of Dark Channel Prior and Guided Filter. The Saliency map is then used to segment the improved image and identify passing vehicles. Lastly, we describe our method for calculating the actual distance in units from a camera-equipped vehicle of an item (another vehicle).Our proposed solution can warn the driver based on the distance to help them prevent an accident. Our suggested technology improves images and accurately detects vehicles nearly 100% of the time
Semi-supervised Transfer Learning for Image Rain Removal
Single image rain removal is a typical inverse problem in computer vision.
The deep learning technique has been verified to be effective for this task and
achieved state-of-the-art performance. However, previous deep learning methods
need to pre-collect a large set of image pairs with/without synthesized rain
for training, which tends to make the neural network be biased toward learning
the specific patterns of the synthesized rain, while be less able to generalize
to real test samples whose rain types differ from those in the training data.
To this issue, this paper firstly proposes a semi-supervised learning paradigm
toward this task. Different from traditional deep learning methods which only
use supervised image pairs with/without synthesized rain, we further put real
rainy images, without need of their clean ones, into the network training
process. This is realized by elaborately formulating the residual between an
input rainy image and its expected network output (clear image without rain) as
a specific parametrized rain streaks distribution. The network is therefore
trained to adapt real unsupervised diverse rain types through transferring from
the supervised synthesized rain, and thus both the short-of-training-sample and
bias-to-supervised-sample issues can be evidently alleviated. Experiments on
synthetic and real data verify the superiority of our model compared to the
state-of-the-arts.Comment: 10 page