4,455 research outputs found
Minimum entropy restoration using FPGAs and high-level techniques
One of the greatest perceived barriers to the widespread use of FPGAs in image processing is the difficulty for application specialists of developing algorithms on reconfigurable hardware. Minimum entropy deconvolution (MED) techniques have been shown to be effective in the restoration of star-field images. This paper reports on an attempt to implement a MED algorithm using simulated annealing, first on a microprocessor, then on an FPGA. The FPGA implementation uses DIME-C, a C-to-gates compiler, coupled with a low-level core library to simplify the design task. Analysis of the C code and output from the DIME-C compiler guided the code optimisation. The paper reports on the design effort that this entailed and the resultant performance improvements
Mammographic image restoration using maximum entropy deconvolution
An image restoration approach based on a Bayesian maximum entropy method
(MEM) has been applied to a radiological image deconvolution problem, that of
reduction of geometric blurring in magnification mammography. The aim of the
work is to demonstrate an improvement in image spatial resolution in realistic
noisy radiological images with no associated penalty in terms of reduction in
the signal-to-noise ratio perceived by the observer. Images of the TORMAM
mammographic image quality phantom were recorded using the standard
magnification settings of 1.8 magnification/fine focus and also at 1.8
magnification/broad focus and 3.0 magnification/fine focus; the latter two
arrangements would normally give rise to unacceptable geometric blurring.
Measured point-spread functions were used in conjunction with the MEM image
processing to de-blur these images. The results are presented as comparative
images of phantom test features and as observer scores for the raw and
processed images. Visualization of high resolution features and the total image
scores for the test phantom were improved by the application of the MEM
processing. It is argued that this successful demonstration of image
de-blurring in noisy radiological images offers the possibility of weakening
the link between focal spot size and geometric blurring in radiology, thus
opening up new approaches to system optimization.Comment: 18 pages, 10 figure
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
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
In this paper, we propose a very deep fully convolutional encoding-decoding
framework for image restoration such as denoising and super-resolution. The
network is composed of multiple layers of convolution and de-convolution
operators, learning end-to-end mappings from corrupted images to the original
ones. The convolutional layers act as the feature extractor, which capture the
abstraction of image contents while eliminating noises/corruptions.
De-convolutional layers are then used to recover the image details. We propose
to symmetrically link convolutional and de-convolutional layers with skip-layer
connections, with which the training converges much faster and attains a
higher-quality local optimum. First, The skip connections allow the signal to
be back-propagated to bottom layers directly, and thus tackles the problem of
gradient vanishing, making training deep networks easier and achieving
restoration performance gains consequently. Second, these skip connections pass
image details from convolutional layers to de-convolutional layers, which is
beneficial in recovering the original image. Significantly, with the large
capacity, we can handle different levels of noises using a single model.
Experimental results show that our network achieves better performance than all
previously reported state-of-the-art methods.Comment: Accepted to Proc. Advances in Neural Information Processing Systems
(NIPS'16). Content of the final version may be slightly different. Extended
version is available at http://arxiv.org/abs/1606.0892
Learning Deep CNN Denoiser Prior for Image Restoration
Model-based optimization methods and discriminative learning methods have
been the two dominant strategies for solving various inverse problems in
low-level vision. Typically, those two kinds of methods have their respective
merits and drawbacks, e.g., model-based optimization methods are flexible for
handling different inverse problems but are usually time-consuming with
sophisticated priors for the purpose of good performance; in the meanwhile,
discriminative learning methods have fast testing speed but their application
range is greatly restricted by the specialized task. Recent works have revealed
that, with the aid of variable splitting techniques, denoiser prior can be
plugged in as a modular part of model-based optimization methods to solve other
inverse problems (e.g., deblurring). Such an integration induces considerable
advantage when the denoiser is obtained via discriminative learning. However,
the study of integration with fast discriminative denoiser prior is still
lacking. To this end, this paper aims to train a set of fast and effective CNN
(convolutional neural network) denoisers and integrate them into model-based
optimization method to solve other inverse problems. Experimental results
demonstrate that the learned set of denoisers not only achieve promising
Gaussian denoising results but also can be used as prior to deliver good
performance for various low-level vision applications.Comment: Accepted to CVPR 2017. Code: https://github.com/cszn/ircn
Analysis of requirements for computer control and data processing experiment subsystems: S-056 experiment. Image data processing system software calibration and application sequence study
Software calibration data and an optimum sequence for image processing technique application are developed for use in processing of S-056 telescope images
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