Dynamic Non-Regular Sampling Sensor Using Frequency Selective Reconstruction

Jonscher, Markus; Seiler, Jurgen; Lanz, Daniela; Schöberl, Michael; Bätz, Michel; Kaup, André

doi:10.1109/tcsvt.2018.2876653

Cited by 3 publications

(4 citation statements)

References 31 publications

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“…Its results are in accordance with the PSNR values and the averages are provided in the last row of Tables 1 and 2 for completeness. Comparing the average results from both tables, we find that using a dynamic mask outperforms using a fixed mask by roughly +1 dB which is consistent with the findings in [16]. In Table 1, we can observe that D-FSR + RME, outperforms the original version from [15] by +1.01 dB for the Spincalendar sequence.…”

Section: Simulations and Resultssupporting

confidence: 82%

“…Besides still images, the acquisition of video data is of great importance. In combination with quarter sampling, video acquisi- tion has been investigated for fixed quarter sampling masks [14,15] as well as for dynamic quarter sampling masks [16]. For the latter, the sampling mask changes from frame to frame and a sophisticated read-out strategy is applied such that each pixel in a 2×2 block is read exactly once within four frames.…”

Section: Introductionmentioning

confidence: 99%

“…Every fourth frame, the mask repeats. Compared to a purely spatial or purely temporal sub-sampling, a more uniform placement of the pixels in time and space is achieved and a higher reconstruction quality is found [16].…”

Section: Introductionmentioning

confidence: 99%

“…As second contribution, we propose the so called D-FSR being a new implementation of recursive FSR that handles the projected pixel differently than in [15] and can be used with dynamic sampling masks. It is different to [15], where only fixed masks are considered and it is different from [16], where information from future frames is used.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Novel Consistency Check For Fast Recursive Reconstruction Of Non-Regularly Sampled Video Data

Grosche¹,

Seiler²,

Kaup³

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Quarter sampling is a novel sensor design that allows for an acquisition of higher resolution images without increasing the number of pixels. When being used for video data, one out of four pixels is measured in each frame. Effectively, this leads to a non-regular spatio-temporal sub-sampling. Compared to purely spatial or temporal sub-sampling, this allows for an increased reconstruction quality, as aliasing artifacts can be reduced. For the fast reconstruction of such sensor data with a fixed mask, recursive variant of frequency selective reconstruction (FSR) was proposed. Here, pixels measured in previous frames are projected into the current frame to support its reconstruction. In doing so, the motion between the frames is computed using template matching. Since some of the motion vectors may be erroneous, it is important to perform a proper consistency checking. In this paper, we propose faster consistency checking methods as well as a novel recursive FSR that uses the projected pixels different than in literature and can handle dynamic masks. Altogether, we are able to significantly increase the reconstruction quality by +1.01 dB compared to the state-of-the-art recursive reconstruction method using a fixed mask. Compared to a single frame reconstruction, an average gain of about +1.52 dB is achieved for dynamic masks. At the same time, the computational complexity of the consistency checks is reduced by a factor of 13 compared to the literature algorithm.

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Section: Simulations and Resultssupporting

confidence: 82%