153 research outputs found
Improving Image Restoration with Soft-Rounding
Several important classes of images such as text, barcode and pattern images
have the property that pixels can only take a distinct subset of values. This
knowledge can benefit the restoration of such images, but it has not been
widely considered in current restoration methods. In this work, we describe an
effective and efficient approach to incorporate the knowledge of distinct pixel
values of the pristine images into the general regularized least squares
restoration framework. We introduce a new regularizer that attains zero at the
designated pixel values and becomes a quadratic penalty function in the
intervals between them. When incorporated into the regularized least squares
restoration framework, this regularizer leads to a simple and efficient step
that resembles and extends the rounding operation, which we term as
soft-rounding. We apply the soft-rounding enhanced solution to the restoration
of binary text/barcode images and pattern images with multiple distinct pixel
values. Experimental results show that soft-rounding enhanced restoration
methods achieve significant improvement in both visual quality and quantitative
measures (PSNR and SSIM). Furthermore, we show that this regularizer can also
benefit the restoration of general natural images.Comment: 9 pages, 6 figure
Text Image Deblurring Using Kernel Sparsity Prior
Previous methods on text image motion deblurring seldom consider the sparse characteristics of the blur kernel. This paper proposes a new text image motion deblurring method by exploiting the sparse properties of both text image itself and kernel. It incorporates the Lâ‚€-norm for regularizing the blur kernel in the deblurring model, besides the Lâ‚€ sparse priors for the text image and its gradient. Such a Lâ‚€-norm-based model is efficiently optimized by half-quadratic splitting coupled with the fast conjugate descent method. To further improve the quality of the recovered kernel, a structure-preserving kernel denoising method is also developed to filter out the noisy pixels, yielding a clean kernel curve. Experimental results show the superiority of the proposed method. The source code and results are available at: https://github.com/shenjianbing/text-image-deblur
Light Field Blind Motion Deblurring
We study the problem of deblurring light fields of general 3D scenes captured
under 3D camera motion and present both theoretical and practical
contributions. By analyzing the motion-blurred light field in the primal and
Fourier domains, we develop intuition into the effects of camera motion on the
light field, show the advantages of capturing a 4D light field instead of a
conventional 2D image for motion deblurring, and derive simple methods of
motion deblurring in certain cases. We then present an algorithm to blindly
deblur light fields of general scenes without any estimation of scene geometry,
and demonstrate that we can recover both the sharp light field and the 3D
camera motion path of real and synthetically-blurred light fields.Comment: To be presented at CVPR 201
Blind Image Deblurring via Reweighted Graph Total Variation
Blind image deblurring, i.e., deblurring without knowledge of the blur
kernel, is a highly ill-posed problem. The problem can be solved in two parts:
i) estimate a blur kernel from the blurry image, and ii) given estimated blur
kernel, de-convolve blurry input to restore the target image. In this paper, by
interpreting an image patch as a signal on a weighted graph, we first argue
that a skeleton image---a proxy that retains the strong gradients of the target
but smooths out the details---can be used to accurately estimate the blur
kernel and has a unique bi-modal edge weight distribution. We then design a
reweighted graph total variation (RGTV) prior that can efficiently promote
bi-modal edge weight distribution given a blurry patch. However, minimizing a
blind image deblurring objective with RGTV results in a non-convex
non-differentiable optimization problem. We propose a fast algorithm that
solves for the skeleton image and the blur kernel alternately. Finally with the
computed blur kernel, recent non-blind image deblurring algorithms can be
applied to restore the target image. Experimental results show that our
algorithm can robustly estimate the blur kernel with large kernel size, and the
reconstructed sharp image is competitive against the state-of-the-art methods.Comment: 5 pages, submitted to IEEE International Conference on Acoustics,
Speech and Signal Processing, Calgary, Alberta, Canada, April, 201
Neuromorphic Imaging with Joint Image Deblurring and Event Denoising
Neuromorphic imaging reacts to per-pixel brightness changes of a dynamic
scene with high temporal precision and responds with asynchronous streaming
events as a result. It also often supports a simultaneous output of an
intensity image. Nevertheless, the raw events typically involve a great amount
of noise due to the high sensitivity of the sensor, while capturing fast-moving
objects at low frame rates results in blurry images. These deficiencies
significantly degrade human observation and machine processing. Fortunately,
the two information sources are inherently complementary -- events with
microsecond temporal resolution, which are triggered by the edges of objects
that are recorded in latent sharp images, can supply rich motion details
missing from the blurry images. In this work, we bring the two types of data
together and propose a simple yet effective unifying algorithm to jointly
reconstruct blur-free images and noise-robust events, where an
event-regularized prior offers auxiliary motion features for blind deblurring,
and image gradients serve as a reference to regulate neuromorphic noise
removal. Extensive evaluations on real and synthetic samples present our
superiority over other competing methods in restoration quality and greater
robustness to some challenging realistic scenarios. Our solution gives impetus
to the improvement of both sensing data and paves the way for highly accurate
neuromorphic reasoning and analysis.Comment: Submitted to TI
- …