Recursive Frequency Selective Reconstruction of Non-Regularly Sampled Video Data
arXiv:2204.03277 · doi:10.1109/PCS.2016.7906325
Abstract
High resolution images can be acquired using a non-regular sampling sensor which consists of an underlying low resolution sensor that is covered with a non-regular sampling mask. The reconstructed high resolution image is then obtained during post-processing. Recently, it has been shown that the temporal correlation between neighboring frames can be exploited in order to enhance the reconstruction quality of non-regularly sampled video data. In this paper, a new recursive multi-frame reconstruction approach is proposed in order to further increase the reconstruction quality. By using a new reference order, previously reconstructed frames can be used for the subsequent motion estimation and a new weighting function allows for the incorporation of multiple pixels projected onto the same position. With the new recursive multi-frame approach, a visually noticeable average gain in PSNR of up to 1.13 dB with respect to a state-of-the-art single-frame reconstruction approach can be achieved. Compared to the existing multi-frame approach, a gain of 0.31 dB is possible. SSIM results show the same behavior as PSNR results. Additionally, the pre-reconstruction step of the existing multi-frame approach can be avoided and the new algorithm is, in general, capable of real-time processing.
5 pages, 7 figures, 3 tables, Picture Coding Symposium (PCS)
References in corpus (4)
- Resampling Images to a Regular Grid from a Non-Regular Subset of Pixel Positions Using Frequency Selective Reconstruction
- Reconstruction of images taken by a pair of non-regular sampling sensors using correlation based matching
- Reconstruction of Videos Taken by a Non-Regular Sampling Sensor
- Texture-Dependent Frequency Selective Reconstruction of Non-Regularly Sampled Images