585 research outputs found
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
An Improved Adaptive Deconvolution Algorithm for Single Image Deblurring
One of the most common defects in digital photography is motion blur caused by camera shake. Shift-invariant motion blur can be modeled as a convolution of the true latent image and a point spread function (PSF) with additive noise. The goal of image deconvolution is to reconstruct a latent image from a degraded image. However, ringing is inevitable artifacts arising in the deconvolution stage. To suppress undesirable artifacts, regularization based methods have been proposed using natural image priors to overcome the ill-posedness of deconvolution problem. When the estimated PSF is erroneous to some extent or the PSF size is large, conventional regularization to reduce ringing would lead to loss of image details.
This paper focuses on the nonblind deconvolution by adaptive regularization which preserves image details, while suppressing ringing artifacts. The way is to control the regularization weight adaptively according to the image local characteristics. We adopt elaborated reference maps that indicate the edge strength so that textured and smooth regions can be distinguished. Then we impose an appropriate constraint on the optimization process. The experiments’ results on both synthesized and real images show that our method can restore latent image with much fewer ringing and favors the sharp edges
- …