Abstract
Compressive sensing (CS) theory has opened up new paths for the development of signal processing applications. Based on this theory, a novel single pixel camera architecture has been introduced to overcome the current limitations and challenges of traditional focal plane arrays. However, video quality based on this method is limited by existing acquisition and recovery methods, and the method also suffers from being time-consuming. In this paper, a multi-frame motion estimation algorithm is proposed in CS video to enhance the video quality. The proposed algorithm uses multiple frames to implement motion estimation. Experimental results show that using multi-frame motion estimation can improve the quality of recovered videos. To further reduce the motion estimation time, a block match algorithm is used to process motion estimation. Experiments demonstrate that using the block match algorithm can reduce motion estimation time by 30%.
Highlights
Compressive sensing is a novel sampling theory
The mentioned methods treat scenes as a sequence of static frames as opposed to a continuously changing scene. Another method is based on modeling specific video sequence evolution as a linear dynamical system (LDS) that requires a considerable number of samples for reconstruction [7]
Many video sampling schemes based on compressive sensing do not take into account the errors caused by violating the static scene assumption
Summary
Compressive sensing is a novel sampling theory. According to this theory, a small group of non-adaptive linear projections of a compressible signal contain enough information for image reconstruction and processing. By consideration of a new theory of CS that combines sampling and compression procedures, this sensing method can recover a static image [2]. One direct way to implement CS video is to reconstruct a series of images by applying the recovery method to each frame independently These methods do not utilize the motion information between each frame and have poor performance in terms of recovered video quality. The mentioned methods treat scenes as a sequence of static frames as opposed to a continuously changing scene Another method is based on modeling specific video sequence evolution as a linear dynamical system (LDS) that requires a considerable number of samples for reconstruction [7]. We utilize multi-frame motion estimation to improve the video quality. Experimental results will be shown to verify the performance of our proposed scheme
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