Robust velocity and position estimation is crucial for autonomous robot navigation. The optical flow based methods for autonomous navigation have been receiving increasing attentions in tandem with the development of micro unmanned aerial vehicles. This paper proposes a kernel cross-correlator (KCC) based algorithm to determine optical flow using a monocular camera, which is named as correlatio...

Conclusions; We propose a fully digital auto-focusing system based on novel out-of-focus blur estimation and restoration algorithms. The main advantages of the proposed PSF estimation algorithm are that it can estimate both radius and sample values of an arbitrary circularly symmetric blur, and that it does not require the DFT or a numerical optimisation process for parameter estimation. As a r...

In the manuscript, we discuss the blur kernel measurement giving the ground truth kernel and propose an effective kernel similarity (KS) metric. In this section, we provide examples and more results to justify the proposed kernel similarity metric in Section 3 of the manuscript. A good metric for kernel similarity should be shift and range (i.e., the size of kernel) invariant. Figure 1 shows th...

We present a deblurring method for scenes with occluding objects using a carefully designed layered blur model. Layered blur model is frequently used in the motion deblurring problem to handle locally varying blurs, which is caused by object motions or depth variations in a scene. However, conventional models have a limitation in representing the layer interactions occurring at occlusion bounda...

This paper presents a novel non-iterative method to restore the out-of-focus part of an image. The proposed method first applies a robust local blur estimation to obtain a blur map of the image. The estimation uses the maximum of difference ratio between the original image and its two digitally re-blurred versions to estimate the local blur radius. Then adaptive least mean square filters based ...

Blind image deblurring is an important but challenging problem in processing. Traditional optimization-based methods typically formulate this task as a maximum-a-posteriori estimation or variational inference problem, whose performance highly relies on handcrafted priors for both the latent and blur kernel. In contrast, recent deep learning generally learn from large collection of training imag...

Previously, Raskar proposed a method of transforming the frequency response of a traditional shutter through the use of coded exposure. This technique shaped the frequency spectrum to a broadband signal that preserved higher frequency information and thus made the deconvolution process more well posed. This paper presents a generalization of the motion blur kernel he used. Instead of requiring ...

Recent research showed that the dual-pixel sensor has made great progress in defocus map estimation and image deblurring. However, extracting real-time views is troublesome complex algorithm deployment. Moreover, deblurred generated by deblurring network lacks high-frequency details, which unsatisfactory human perception. To overcome this issue, we propose a novel method uses guidance of to imp...

Object movement during exposure generates blur. Removing blur is challenging because one has to estimate the motion blur, which can spatially vary over the image. Even if the motion is successfully identified, blur removal can be unstable because the blur kernel attenuates high frequency image contents. We address the problem of removing blur from objects moving at constant velocities in arbitr...

Recent blind deconvolution methods rely on either salient edges or the power spectrum of the input image for estimating the blur kernel, but not both. In this work we show that the two methods are inherently complimentary to each other. Edge-based methods work well for images containing large salient structures, but fail on small-scale textures. Power-spectrum-based methods, on the contrary, ar...