4 research outputs found
Multi-scale Processing of Noisy Images using Edge Preservation Losses
Noisy images processing is a fundamental task of computer vision. The first
example is the detection of faint edges in noisy images, a challenging problem
studied in the last decades. A recent study introduced a fast method to detect
faint edges in the highest accuracy among all the existing approaches. Their
complexity is nearly linear in the image's pixels and their runtime is seconds
for a noisy image. Their approach utilizes a multi-scale binary partitioning of
the image. By utilizing the multi-scale U-net architecture, we show in this
paper that their method can be dramatically improved in both aspects of run
time and accuracy. By training the network on a dataset of binary images, we
developed an approach for faint edge detection that works in a linear
complexity. Our runtime of a noisy image is milliseconds on a GPU. Even though
our method is orders of magnitude faster, we still achieve higher accuracy of
detection under many challenging scenarios. In addition, we show that our
approach to performing multi-scale preprocessing of noisy images using U-net
improves the ability to perform other vision tasks under the presence of noise.
We prove it on the problems of noisy objects classification and classical image
denoising. We show that multi-scale denoising can be carried out by a novel
edge preservation loss. As our experiments show, we achieve high-quality
results in the three aspects of faint edge detection, noisy image
classification and natural image denoising
SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution
Single image super resolution (SISR) is to reconstruct a high resolution
image from a single low resolution image. The SISR task has been a very
attractive research topic over the last two decades. In recent years,
convolutional neural network (CNN) based models have achieved great performance
on SISR task. Despite the breakthroughs achieved by using CNN models, there are
still some problems remaining unsolved, such as how to recover high frequency
details of high resolution images. Previous CNN based models always use a pixel
wise loss, such as l2 loss. Although the high resolution images constructed by
these models have high peak signal-to-noise ratio (PSNR), they often tend to be
blurry and lack high-frequency details, especially at a large scaling factor.
In this paper, we build a super resolution perceptual generative adversarial
network (SRPGAN) framework for SISR tasks. In the framework, we propose a
robust perceptual loss based on the discriminator of the built SRPGAN model. We
use the Charbonnier loss function to build the content loss and combine it with
the proposed perceptual loss and the adversarial loss. Compared with other
state-of-the-art methods, our method has demonstrated great ability to
construct images with sharp edges and rich details. We also evaluate our method
on different benchmarks and compare it with previous CNN based methods. The
results show that our method can achieve much higher structural similarity
index (SSIM) scores on most of the benchmarks than the previous state-of-art
methods
Connecting Image Denoising and High-Level Vision Tasks via Deep Learning
Image denoising and high-level vision tasks are usually handled independently
in the conventional practice of computer vision, and their connection is
fragile. In this paper, we cope with the two jointly and explore the mutual
influence between them with the focus on two questions, namely (1) how image
denoising can help improving high-level vision tasks, and (2) how the semantic
information from high-level vision tasks can be used to guide image denoising.
First for image denoising we propose a convolutional neural network in which
convolutions are conducted in various spatial resolutions via downsampling and
upsampling operations in order to fuse and exploit contextual information on
different scales. Second we propose a deep neural network solution that
cascades two modules for image denoising and various high-level tasks,
respectively, and use the joint loss for updating only the denoising network
via back-propagation. We experimentally show that on one hand, the proposed
denoiser has the generality to overcome the performance degradation of
different high-level vision tasks. On the other hand, with the guidance of
high-level vision information, the denoising network produces more visually
appealing results. Extensive experiments demonstrate the benefit of exploiting
image semantics simultaneously for image denoising and high-level vision tasks
via deep learning. The code is available online:
https://github.com/Ding-Liu/DeepDenoisingComment: arXiv admin note: text overlap with arXiv:1706.0428
A Multiscale Image Denoising Algorithm Based On Dilated Residual Convolution Network
Image denoising is a classical problem in low level computer vision.
Model-based optimization methods and deep learning approaches have been the two
main strategies for solving the problem. Model-based optimization methods are
flexible for handling different inverse problems but are usually
time-consuming. In contrast, deep learning methods have fast testing speed but
the performance of these CNNs is still inferior. To address this issue, here we
propose a novel deep residual learning model that combines the dilated residual
convolution and multi-scale convolution groups. Due to the complex patterns and
structures of inside an image, the multiscale convolution group is utilized to
learn those patterns and enlarge the receptive field. Specifically, the
residual connection and batch normalization are utilized to speed up the
training process and maintain the denoising performance. In order to decrease
the gridding artifacts, we integrate the hybrid dilated convolution design into
our model. To this end, this paper aims to train a lightweight and effective
denoiser based on multiscale convolution group. Experimental results have
demonstrated that the enhanced denoiser can not only achieve promising
denoising results, but also become a strong competitor in practical
application