Deep implicit surfaces excel at modeling generic shapes but do not always capture the regularities present in manufactured objects, which is something simple geometric primitives are particularly good at. In this paper, we propose a representation combining latent and explicit parameters that can be decoded into a set of deep implicit and geometric shapes that are consistent with each other. As a result, we can effectively model both complex and highly regular shapes that coexist in manufactured objects. This enables our approach to manipulate 3D shapes in an efficient and precise manner.

Blind motion deblurring methods are primarily responsible for recovering an accurate estimate of the blur kernel. Non-blind deblurring (NBD) methods, on the other hand, attempt to faithfully restore the original image, given the blur estimate. However, NBD is quite susceptible to errors in blur kernel. In this work, we present a convolutional neural network-based approach to handle kernel uncertainty in non-blind motion deblurring. We provide multiple latent image estimates corresponding to different prior strengths obtained from a given blurry observation in order to exploit the complementarity of these inputs for improved learning. To generalize the performance to tackle arbitrary kernel noise, we train our network with a large number of real and synthetic noisy blur kernels. Our network mitigates the effects of kernel noise so as to yield detail-preserving and artifact-free restoration. Our quantitative and qualitative evaluations on benchmark datasets demonstrate that the proposed method delivers state-of-the-art results. To further underscore the benefits that can be achieved from our network, we propose two adaptations of our method to improve kernel estimates, and image deblurring quality, respectively.

A vast majority of contemporary cameras employ rolling shutter (RS) mechanism to capture images. Due to the sequential mechanism, images acquired with a moving camera are subjected to rolling shutter effect which manifests as geometric distortions. In this work, we consider the specific scenario of a fast moving camera wherein the rolling shutter distortions not only are predominant but also become depth-dependent which in turn results in intra-frame occlusions. To this end, we develop a first-of-its-kind pipeline to recover the latent image of a 3D scene from a set of such RS distorted images. The proposed approach sequentially recovers both the camera motion and scene structure while accounting for RS and occlusion effects. Subsequently, we perform depth and occlusion-aware rectification of RS images to yield the desired latent image. Our experiments on synthetic and real image sequences reveal that the proposed approach achieves state-of-the-art results.

Images captured from consumer cameras are often prone to camera shake resulting in motion blur. Effect of motion blur is more common in high dynamic range imaging applications where multiple images are captured over a wide range of exposure settings. In this paper, we propose a unified approach to perform high dynamic range super-resolution (HDR-SR) imaging from a sequence of low dynamic range and low-resolution motion-blurred images. While existing works on HDR-SR assume the availability of blur-free input images, we propose an approach which is designed to handle blurring effects caused by the camera motion. Our approach attempts to harness the complementarity present in terms of the sensor exposure and blur to yield a high-quality image which has both higher spatial resolution as well as dynamic range. Experiments on synthetic and real examples demonstrate that the proposed method delivers state-of-the-art results.

Inevitable camera motion during exposure does not augur well for free-hand photography. Distortions introduced in images can be of different types and mainly depend on the structure of the scene, the nature of camera motion, and the shutter mechanism of the camera. In this paper, we address the problem of registering images taken from global shutter and rolling shutter cameras and reveal the constraints on camera motion that admit registration, change detection, and rectification. Our analysis encompasses degradations arising from camera motion during exposure and differences in shutter mechanisms. We also investigate conditions under which camera motions causing distortions in reference and target image can be decoupled to yield the underlying latent image through RS rectification. We validate our approach using several synthetic and real examples.

In this work, we investigate the relation between the edge profiles present in a motion blurred image and the underlying camera motion responsible for causing the motion blur. While related works on camera motion estimation (CME) rely on the strong assumption of space-invariant blur, we handle the challenging case of general camera motion. We first show how edge profiles alone can be harnessed to perform direct CME from a single observation. While it is routine for conventional methods to jointly estimate the latent image too through alternating minimization, our above scheme is best-suited when such a pursuit is either impractical or inefficacious. For applications that actually favor an alternating minimization strategy, the edge profiles can serve as a valuable cue. We incorporate a suitably derived constraint from edge profiles into an existing blind deblurring framework and demonstrate improved restoration performance. Experiments reveal that this approach yields state-of-the-art results for the blind deblurring problem.

We propose a framework for robust estimation of normal of a planar scene from a single motion blurred observation. We first reveal how feature points can be extracted from blur kernels and matched to generate several point correspondences. Although these points correspond to different homographies, the fact that they conform to the same normal yields a rank-3 constraint which we harness within a hierarchical clustering framework to estimate the normal accurately.