Sparsity-based defect pixel compensation for arbitrary camera raw images

Schöberl, Michael; Seiler, Jurgen; Kasper, Bernhard; Foessel, Siegfried; Kaup, André

doi:10.1109/icassp.2011.5946639

Cited by 10 publications

(15 citation statements)

References 8 publications

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“…The fully reconstructed framesft[m, n] are then obtained by applying Frequency Selective Extrapolation (FSE) independently on all frames ft [m, n]. Up to this step, this corresponds to the reconstruction of single frames [3] and is from now on referred to as FSE-SF. FSE iteratively generates the sparse signal model…”

Section: State-of-the-art Single-frame Reconstructionmentioning

confidence: 99%

“…One possibility to achieve this may be single-image super-resolution where a reasonable HR image can be obtained from a single LR image [1], [2]. Another possibility to get an HR image has been shown in [3] where an LR sensor is covered with a non-regular sampling mask. The incomplete HR image that is captured this way has to be reconstructed afterwards.…”

Section: Introductionmentioning

confidence: 99%

“…Since the per-pixel area sensitive to light of such a covered LR sensor is equal to that of an HR sensor, the noise behavior is similar. As mentioned in [3], Frequency Selective Extrapolation (FSE) is used for the reconstruction of these missing pixels. This leads to the full HR imagef [m, n] on the right where reconstructed pixels are denoted in dark-gray.…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of videos taken by a non-regular sampling sensor

Jonscher¹,

Seiler²,

Bätz³

et al. 2015

2015 Visual Communications and Image Processing (VCIP)

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Recently, it has been shown that a high resolution image can be obtained without the usage of a high resolution sensor. The main idea has been that a low resolution sensor is covered with a nonregular sampling mask followed by a reconstruction of the incomplete high resolution image captured this way. In this paper, a multi-frame reconstruction approach is proposed where a video is taken by a nonregular sampling sensor and fully reconstructed afterwards. By utilizing the temporal correlation between neighboring frames, the reconstruction quality can be further enhanced. Compared to a state-of-the-art singleframe reconstruction approach, this leads to a visually noticeable gain in PSNR of up to 1.19 dB on average.

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Section: State-of-the-art Single-frame Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconstruction of videos taken by a non-regular sampling sensor

Jonscher¹,

Seiler²,

Bätz³

et al. 2015

2015 Visual Communications and Image Processing (VCIP)

Self Cite

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“…The overhead of reading and discarding pixels is only acceptable for a very low number of defect pixels. We show the application of sparsity-based defect interpolation on Bayer pattern raw data in [6].…”

Section: Spatial Domain Image Samplingmentioning

confidence: 99%

Increasing imaging resolution by covering your sensor

Schöberl

Seller

Foessel

et al. 2011

2011 18th IEEE International Conference on Image Processing

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Up to now, an increase in camera resolution required image sensors with more and more pixels. However, acquisition systems are limited in their pixels per second throughput given as power and complexity constraints. Simply capturing more pixels in a given system is often not possible. We propose a new non-regular imaging architecture that samples only few pixels and reconstructs a high resolution image afterwards. Our sampling is optimized to provide non-regular spatial sampling from a sensor with regular readout circuits. An existing slow image acquisition system can then be used to capture the data. The image reconstruction is performed with a local sparsity-based approach. The result is a high resolution image that requires a much smaller effort during acquisition

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“…This algorithm has received several enhancements [7], [39], [40] in order to improve the extrapolation quality and has been applied to several signal extrapolation tasks in the area of image and video signal processing. To name just a few, this has been applications like error concealment in video communication [41] or defect pixel compensation [42]. However, unfortunately Frequency Selective…”

Section: B Frequency Selective Reconstruction Algorithmmentioning

confidence: 99%

Resampling Images to a Regular Grid From a Non-Regular Subset of Pixel Positions Using Frequency Selective Reconstruction

Seiler

Jonscher

Schöberl

et al. 2015

IEEE Trans. on Image Process.

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100

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Even though image signals are typically defined on a regular 2D grid, there also exist many scenarios where this is not the case and the amplitude of the image signal only is available for a non-regular subset of pixel positions. In such a case, a resampling of the image to a regular grid has to be carried out. This is necessary since almost all algorithms and technologies for processing, transmitting or displaying image signals rely on the samples being available on a regular grid. Thus, it is of great importance to reconstruct the image on this regular grid, so that the reconstruction comes closest to the case that the signal has been originally acquired on the regular grid. In this paper, Frequency Selective Reconstruction is introduced for solving this challenging task. This algorithm reconstructs image signals by exploiting the property that small areas of images can be represented sparsely in the Fourier domain. By further considering the basic properties of the optical transfer function of imaging systems, a sparse model of the signal is iteratively generated. In doing so, the proposed algorithm is able to achieve a very high reconstruction quality, in terms of peak signal-to-noise ratio (PSNR) and structural similarity measure as well as in terms of visual quality. The simulation results show that the proposed algorithm is able to outperform state-of-the-art reconstruction algorithms and gains of more than 1 dB PSNR are possible.

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Sparsity-based defect pixel compensation for arbitrary camera raw images

Cited by 10 publications

References 8 publications

Reconstruction of videos taken by a non-regular sampling sensor

Reconstruction of videos taken by a non-regular sampling sensor

Increasing imaging resolution by covering your sensor

Resampling Images to a Regular Grid From a Non-Regular Subset of Pixel Positions Using Frequency Selective Reconstruction

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