Research On Video Planar Motion Deblurring And Super Resolution

This thesis is concerned with super resolution and image blind restoration, one of the most challenging problems in Computer Vision. Mathematically, both super resolution and image blind restoration are inherently ill-posed inverse problem. Such problems may be tackled by constraining the solution space according to a priori knowledge of the form of the solution.This dissertation first reviews the super resolution approaches and image deblur approaches, then presents a new robust super resolution algorithm of compressed video and a new planar motion deblurring method from dual images using transparency.Before super resolution with multiple frames, we first isolate the frames individually, and get their corresponding initial super resolution estimates within the Bayesian framework by exploiting the information available in the compressed bitstream. Then a final SR image can be obtained with the neighboring initial estimates. In details:1. It formulates the process from HR image to the corresponding decompressed LR frame di-rectely to exclude the register errors between the object hr image and the neighbouring im-ages.2. It presents a new latent image prior, which has two components:the global prior, which leads to edge-preserving SR images, and the local prior, which leads to SR images with less ringing artifacts.3. By using the IRLS approach to get a robust cost function to reject outliers, the SR results with little artifacts have been achieved.In view of motion deblurring, it’s contribution includes:1. It proposes a practical algorithm to deblur rigid-body object undergone planar motion from dual conventional images. The planar motion includes rotation and translation. The blur kernel is spatially-variant and unknown. The ambiguities of deblurring planar motion is reduced by adopting a latent binary transparency map prior as well as the information in two motion blurred images. 2. It presents a new motion blur descriptor to model the general planar motion of a (planar) rigid object, which makes our optimized algorithm to be of low computational complexity.3. It reduces user assistance and no user assistance is needed after the fractional transparency map has been calculated.4. It introduces how to modify the Richardson-Lucy algorithm to incorporate our general planar motion blur descriptor.5. It demonstrates that the planar motion RL algorithm can incorporate an iterative reweight-ed least square (IRLS) approach to improve the deblurred results, with two input images complementary to each other.