4,355 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
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification
Convolutional Neural Networks (CNN) are state-of-the-art models for many
image classification tasks. However, to recognize cancer subtypes
automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images
(WSI) is currently computationally impossible. The differentiation of cancer
subtypes is based on cellular-level visual features observed on image patch
scale. Therefore, we argue that in this situation, training a patch-level
classifier on image patches will perform better than or similar to an
image-level classifier. The challenge becomes how to intelligently combine
patch-level classification results and model the fact that not all patches will
be discriminative. We propose to train a decision fusion model to aggregate
patch-level predictions given by patch-level CNNs, which to the best of our
knowledge has not been shown before. Furthermore, we formulate a novel
Expectation-Maximization (EM) based method that automatically locates
discriminative patches robustly by utilizing the spatial relationships of
patches. We apply our method to the classification of glioma and non-small-cell
lung carcinoma cases into subtypes. The classification accuracy of our method
is similar to the inter-observer agreement between pathologists. Although it is
impossible to train CNNs on WSIs, we experimentally demonstrate using a
comparable non-cancer dataset of smaller images that a patch-based CNN can
outperform an image-based CNN
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