1,679 research outputs found
Blur Invariants for Image Recognition
Blur is an image degradation that is difficult to remove. Invariants with
respect to blur offer an alternative way of a~description and recognition of
blurred images without any deblurring. In this paper, we present an original
unified theory of blur invariants. Unlike all previous attempts, the new theory
does not require any prior knowledge of the blur type. The invariants are
constructed in the Fourier domain by means of orthogonal projection operators
and moment expansion is used for efficient and stable computation. It is shown
that all blur invariants published earlier are just particular cases of this
approach. Experimental comparison to concurrent approaches shows the advantages
of the proposed theory.Comment: 15 page
Online Video Deblurring via Dynamic Temporal Blending Network
State-of-the-art video deblurring methods are capable of removing non-uniform
blur caused by unwanted camera shake and/or object motion in dynamic scenes.
However, most existing methods are based on batch processing and thus need
access to all recorded frames, rendering them computationally demanding and
time consuming and thus limiting their practical use. In contrast, we propose
an online (sequential) video deblurring method based on a spatio-temporal
recurrent network that allows for real-time performance. In particular, we
introduce a novel architecture which extends the receptive field while keeping
the overall size of the network small to enable fast execution. In doing so,
our network is able to remove even large blur caused by strong camera shake
and/or fast moving objects. Furthermore, we propose a novel network layer that
enforces temporal consistency between consecutive frames by dynamic temporal
blending which compares and adaptively (at test time) shares features obtained
at different time steps. We show the superiority of the proposed method in an
extensive experimental evaluation.Comment: 10 page
Correct spatially varying image blur by Projective Motion Richardson-Lucy Algorithm and Blur Image alignment
Master'sMASTER OF ENGINEERIN
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Camera Shake Removal With Multiple Images Via Weighted Fourier Burst Accumulation
Blur introduced in an image from camera shake is mostly due to the 3D rotation of the camera. This results in a blur kernel which is non uniform throughout the image. Hence each image in the burst is blurred differently. Various experiments were done to find the deblurred image either with single image or with multiple image. In this paper we analyze multiple image approaches, which capture and combine multiple frames in order to make deblurring more robust and tractable. If the photographer takes many images known as burst, we show that a clear and sharp image can be obtained by combining these multiple images. Also for this work the blurring kernel is unknown (blind) and also it is not found. The methodology used here is Fourier Burst Accumulation which performs a weighted average in Fourier Domain where the weights depend on Fourier spectrum magnitude. In simple words the method can be generalized as Align and Average procedure. Experiments with real camera data and extensive comparisons, show that the proposed burst accumulation algorithm achieves results faster
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