A digital camera samples the continuous real world. As with any sampling process, questions of aliasing for certain sampling frequencies and the prevention thereof arise. In this paper we will discuss the spatial domain sampling and prevention of aliasing in digital cameras. We focus on the widely used birefringent anti alias filters that are often called optical low pass filters (OLPF). We show 2D models for all contributions to spatial domain sampling and derive optimum filter parameters for minimum aliasing and best possible image sharpness. Compared to previously used selection rules, we can show that the optimum selection of filter parameters can easily deliver more sharpness and reduce aliasing by a factor of 2. The simulated results are finally confirmed in real world experiments
A problem often arising in video communication is the reconstruction of missing or distorted areas in a video sequence. Such holes of unavailable pixels may be caused for example by transmission errors of coded video data or undesired objects like logos. In order to close the holes given neighboring available content, a signal extrapolation has to be performed. The best quality can be achieved, if spatial as well as temporal information is used for the reconstruction. However, the question always is in which order to process the extrapolation to obtain the best result. In this paper, an optimized processing order is introduced for improving the extrapolation quality of Three-dimensional Frequency Selective Extrapolation. Using the proposed optimized order, holes in video sequences can be closed from the outer margin to the center, leading to a higher reconstruction quality, and visually noticeable gains of more than 0.5 dB PSNR are possible.
For scalable coding, a high quality of the lowpass band of a wavelet transform is crucial when it is used as a downscaled version of the original signal. However, blur and motion can lead to disturbing artifacts. By incorporating feasible compensation methods directly into the wavelet transform, the quality of the lowpass band can be improved. The displacement in dynamic medical 3-D+t volumes from Computed Tomography is mainly given by expansion and compression of tissue over time and can be modeled well by mesh-based methods. We extend a 2-D mesh-based compensation method to three dimensions to obtain a volume compensation method that can additionally compensate deforming displacements in the third dimension. We show that a 3-D mesh can obtain a higher quality of the lowpass band by 0.28 dB with less than 40% of the model parameters of a comparable 2-D mesh. Results from lossless coding with JPEG 2000 3D and SPECK3D show that the compensated subbands using a 3-D mesh need about 6% less data compared to using a 2-D mesh.
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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