418 research outputs found
Line-Field Based Adaptive Image Model for Blind Deblurring
Ph.DDOCTOR OF PHILOSOPH
IML FISTA: Inexact MuLtilevel FISTA for Image Restoration
This paper presents IML FISTA, a multilevel inertial and inexact
forward-backward algorithm, based on the use of the Moreau envelope to build
efficient and useful coarse corrections. Such construction is provided for a
broad class of composite optimization problems with proximable functions. This
approach is supported by strong theoretical guarantees: we prove both the rate
of convergence and the convergence of the iterates to a minimum in the convex
case, an important result for ill-posed problems. We evaluate our approach on
several image reconstruction problems and we show that it considerably
accelerates the convergence of classical methods such as FISTA, for large-scale
images
Underwater image restoration: super-resolution and deblurring via sparse representation and denoising by means of marine snow removal
Underwater imaging has been widely used as a tool in many fields, however, a major issue is the quality of the resulting images/videos. Due to the light's interaction with water and its constituents, the acquired underwater images/videos often suffer from a significant amount of scatter (blur, haze) and noise. In the light of these issues, this thesis considers problems of low-resolution, blurred and noisy underwater images and proposes several approaches to improve the quality of such images/video frames.
Quantitative and qualitative experiments validate the success of proposed algorithms
Learning a Dilated Residual Network for SAR Image Despeckling
In this paper, to break the limit of the traditional linear models for
synthetic aperture radar (SAR) image despeckling, we propose a novel deep
learning approach by learning a non-linear end-to-end mapping between the noisy
and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is
based on dilated convolutions, which can both enlarge the receptive field and
maintain the filter size and layer depth with a lightweight structure. In
addition, skip connections and residual learning strategy are added to the
despeckling model to maintain the image details and reduce the vanishing
gradient problem. Compared with the traditional despeckling methods, the
proposed method shows superior performance over the state-of-the-art methods on
both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table
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