Motion Deblurring in the Wild

The task of image deblurring is a very ill-posed problem as both the image and the blur are unknown. Moreover, when pictures are taken in the wild, this task becomes even more challenging due to the blur varying spatially and the occlusions between the object. Due to the complexity of the general image model we propose a novel convolutional network architecture which directly generates the sharp image.This network is built in three stages, and exploits the benefits of pyramid schemes often used in blind deconvolution. One of the main difficulties in training such a network is to design a suitable dataset. While useful data can be obtained by synthetically blurring a collection of images, more realistic data must be collected in the wild. To obtain such data we use a high frame rate video camera and keep one frame as the sharp image and frame average as the corresponding blurred image. We show that this realistic dataset is key in achieving state-of-the-art performance and dealing with occlusions

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning is based on a conditional GAN and the content loss . DeblurGAN achieves state-of-the art performance both in the structural similarity measure and visual appearance. The quality of the deblurring model is also evaluated in a novel way on a real-world problem -- object detection on (de-)blurred images. The method is 5 times faster than the closest competitor -- DeepDeblur. We also introduce a novel method for generating synthetic motion blurred images from sharp ones, allowing realistic dataset augmentation. The model, code and the dataset are available at https://github.com/KupynOrest/DeblurGANComment: CVPR 2018 camera-read

Hybrid Approach of Nu-Mob, Mobil and MOBILAP for Face Recognition System

This is the first attempt to systematically address face recognition under (i) non-uniform motion blur and (ii) the combined effects of blur, illumination and pose. In this paper, we propose a methodology for face recognition in the presence of space-varying motion blur comprising of kernels. We model the blurred face as a convex combination of geometrically transformed instances of the focused gallery face, and show that the set of all images obtained by non-uniformly blurring a given image forms a convex set. We first propose a non-uniform blur-robust algorithm by blurring the gallery image’s with its corresponding TSF function and extract LBP features and finally returns the identity of the probe image by comparing the LBP features of the probe image with those of the transformed gallery images and find the closest match. Then we propose the motion blur and illumination-robust algorithm by blurring and re-illuminating the gallery image’s with its corresponding optimal TSF function and illumination coefficients and extract LBP features and finally returns the identity of the probe image. Finally we propose the motion, blur, illumination and pose-robust algorithm by estimating and synthesizing the new pose of the blurred probe image and then blurring and re-illuminating the gallery image’s with its corresponding optimal TSF function and illumination coefficients and extract LBP features and finally finds the closest match of the given input probe image

Generalized Video Deblurring for Dynamic Scenes

Several state-of-the-art video deblurring methods are based on a strong assumption that the captured scenes are static. These methods fail to deblur blurry videos in dynamic scenes. We propose a video deblurring method to deal with general blurs inherent in dynamic scenes, contrary to other methods. To handle locally varying and general blurs caused by various sources, such as camera shake, moving objects, and depth variation in a scene, we approximate pixel-wise kernel with bidirectional optical flows. Therefore, we propose a single energy model that simultaneously estimates optical flows and latent frames to solve our deblurring problem. We also provide a framework and efficient solvers to optimize the energy model. By minimizing the proposed energy function, we achieve significant improvements in removing blurs and estimating accurate optical flows in blurry frames. Extensive experimental results demonstrate the superiority of the proposed method in real and challenging videos that state-of-the-art methods fail in either deblurring or optical flow estimation.Comment: CVPR 2015 ora