531 research outputs found
Convolutional Deblurring for Natural Imaging
In this paper, we propose a novel design of image deblurring in the form of
one-shot convolution filtering that can directly convolve with naturally
blurred images for restoration. The problem of optical blurring is a common
disadvantage to many imaging applications that suffer from optical
imperfections. Despite numerous deconvolution methods that blindly estimate
blurring in either inclusive or exclusive forms, they are practically
challenging due to high computational cost and low image reconstruction
quality. Both conditions of high accuracy and high speed are prerequisites for
high-throughput imaging platforms in digital archiving. In such platforms,
deblurring is required after image acquisition before being stored, previewed,
or processed for high-level interpretation. Therefore, on-the-fly correction of
such images is important to avoid possible time delays, mitigate computational
expenses, and increase image perception quality. We bridge this gap by
synthesizing a deconvolution kernel as a linear combination of Finite Impulse
Response (FIR) even-derivative filters that can be directly convolved with
blurry input images to boost the frequency fall-off of the Point Spread
Function (PSF) associated with the optical blur. We employ a Gaussian low-pass
filter to decouple the image denoising problem for image edge deblurring.
Furthermore, we propose a blind approach to estimate the PSF statistics for two
Gaussian and Laplacian models that are common in many imaging pipelines.
Thorough experiments are designed to test and validate the efficiency of the
proposed method using 2054 naturally blurred images across six imaging
applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin
Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal
Rain is one of the most common weather which can completely degrade the image
quality and interfere with the performance of many computer vision tasks,
especially under heavy rain conditions. We observe that: (i) rain is a mixture
of rain streaks and rainy haze; (ii) the scene depth determines the intensity
of rain streaks and the transformation into the rainy haze; (iii) most existing
deraining methods are only trained on synthetic rainy images, and hence
generalize poorly to the real-world scenes. Motivated by these observations, we
propose a new SEMI-supervised Mixture Of rain REmoval Generative Adversarial
Network (Semi-MoreGAN), which consists of four key modules: (I) a novel
attentional depth prediction network to provide precise depth estimation; (ii)
a context feature prediction network composed of several well-designed detailed
residual blocks to produce detailed image context features; (iii) a pyramid
depth-guided non-local network to effectively integrate the image context with
the depth information, and produce the final rain-free images; and (iv) a
comprehensive semi-supervised loss function to make the model not limited to
synthetic datasets but generalize smoothly to real-world heavy rainy scenes.
Extensive experiments show clear improvements of our approach over twenty
representative state-of-the-arts on both synthetic and real-world rainy images.Comment: 18 page
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