95 research outputs found
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
Fast blind deblurring of QR code images based on adaptive scale control.
With the development of 5G technology, the short delay requirements of commercialization and large amounts of data change our lifestyle day-to-day. In this background, this paper proposes a fast blind deblurring algorithm for QR code images, which mainly achieves the effect of adaptive scale control by introducing an evaluation mechanism. Its main purpose is to solve the out-of-focus caused by lens shake, inaccurate focus, and optical noise by speeding up the latent image estimation in the process of multi-scale division iterative deblurring. The algorithm optimizes productivity under the guidance of collaborative computing, based on the characteristics of the QR codes, such as the features of gradient and strength. In the evaluation step, the Tenengrad method is used to evaluate the image quality, and the evaluation value is compared with the empirical value obtained from the experimental data. Combining with the error correction capability, the recognizable QR codes will be output. In addition, we introduced a scale control parameter to study the relationship between the recognition rate and restoration time. Theoretical analysis and experimental results show that the proposed algorithm has high recovery efficiency and well recovery effect, can be effectively applied in industrial applications
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
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