171 research outputs found
Multimodal Image Denoising based on Coupled Dictionary Learning
In this paper, we propose a new multimodal image denoising approach to
attenuate white Gaussian additive noise in a given image modality under the aid
of a guidance image modality. The proposed coupled image denoising approach
consists of two stages: coupled sparse coding and reconstruction. The first
stage performs joint sparse transform for multimodal images with respect to a
group of learned coupled dictionaries, followed by a shrinkage operation on the
sparse representations. Then, in the second stage, the shrunken
representations, together with coupled dictionaries, contribute to the
reconstruction of the denoised image via an inverse transform. The proposed
denoising scheme demonstrates the capability to capture both the common and
distinct features of different data modalities. This capability makes our
approach more robust to inconsistencies between the guidance and the target
images, thereby overcoming drawbacks such as the texture copying artifacts.
Experiments on real multimodal images demonstrate that the proposed approach is
able to better employ guidance information to bring notable benefits in the
image denoising task with respect to the state-of-the-art.Comment: 2018 IEEE International Conference on Image Processing (ICIP). arXiv
admin note: text overlap with arXiv:1806.0988
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
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
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