Abstract
To improve the spatial resolution of reconstructed images/videos, this paper proposes a Superresolution (SR) reconstruction algorithm based on iterative back projection. In the proposed algorithm, image matching using critical-point filters (CPF) is employed to improve the accuracy of image registration. First, a sliding window is used to segment the video sequence. CPF based image matching is then performed between frames in the window to obtain pixel-level motion fields. Finally, high-resolution (HR) frames are reconstructed based on the motion fields using iterative back projection (IBP) algorithm. The CPF based registration algorithm can adapt to various types of motions in real video scenes. Experimental results demonstrate that, compared to optical flow based image matching with IBP algorithm, subjective quality improvement and an average PSNR score of 0.53 dB improvement are obtained by the proposed algorithm, when applied to video sequence.
Highlights
Since high-resolution (HR) images/videos are important in many applications, such as astronomy, military monitor, medical diagnosis, and remote sensing, superresolution (SR) reconstruction has a great significance in practice [1]
The performance of the proposed algorithm is compared with bilinear interpolation, bicubic interpolation, iterative back projection (IBP) with frequency domain registration (IBPFR) [20], and IBP with optical flow based image matching (IBP-OL) algorithms
To improve the accuracy of image registration, this paper introduces multiresolutional critical-point filters based image matching algorithm (CPF-IM) to image/video superresolution
Summary
Since high-resolution (HR) images/videos are important in many applications, such as astronomy, military monitor, medical diagnosis, and remote sensing, superresolution (SR) reconstruction has a great significance in practice [1]. The concept of superresolution (SR) reconstruction refers to reconstructing a high-resolution (HR) image from one or more low-resolution (LR) images. The purpose of superresolution (SR) reconstruction is using digital image processing algorithm to enhance the spatial resolution by transcending the limiting factors of optical imaging system [2, 3]. Most superresolution reconstruction methods contain four steps: registration, map, interpolation, noise, and blur removal. Registration refers to estimating motion vectors between two different video frames or images. The motion vectors are used to map the pixels of the input lowresolution frames to a common high-resolution reference frame. Noise and blur removal is applied to eliminate the optical sensor blur [4]
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