KEYWORDS: Sensors, Linear filtering, Nonlinear filtering, 3D modeling, Digital filtering, Infrared sensors, Staring arrays, Performance modeling, Systems modeling, Imaging systems
The 3d-noise components are used for describing detector noise, especially for infrared focal-plane array (FPA)
detectors, where individual pixel variations and the read-out-process of pixel values will give rise to detector-induced
intensity variations. For consistency of actual and anticipated system performance it is essential that detector noise is
measured and estimated in a way that is consistent with how noise is modeled in system simulations. This paper
describes how to efficiently obtain bias-free estimates of the 3d-noise components in the frequency domain. Frequency
domain representation of the noise also allows application-tailored detector noise requirements. Intensity variations
caused by e.g. optics should not be allowed to influence the detector noise estimation, frequency domain estimation can
easily avoid influence from such non-uniformity. An alternative approach is to use pre-filtering as a means to suppress
non-uniformity; the effect of filter choice on estimated 3d-detector noise values is examined and filter choice discussed.
Finally, we demonstrate that low pixel operability, requiring replacement by interpolation from neighboring pixels for
many of the pixels in the array, will lead to under-estimation of the detector noise of the operable pixels.
Compression of noisy imagery usually consists of two stages, prefiltering followed by encoding. In this paper we present a technique based on on vector quantization, which combines noise reduction and compression into one step. The idea is to generate a codebook, consisting only of clean image data, which is then used for quantization of the noisy imagery. Simulations performed shows that this approach can efficiently handle images corrupted by noise, and compared to MPEG-4 encoding, this technique, in spite of its simplicity, is the better choice when dealing with high levels of noise.
Wavelet video coding using motion vectors estimated simultaneously at the transmitter and receiver side from the transmitted image data have been reported to have good compression capabilities, comparable to the non-scalable version of H.263. When scalability is required, the comparison turns even more in favor of the wavelet coding scheme. This paper shows that it is possible to reduce the bit-rate further in backward motion estimation schemes by using the certainty of each estimated motion vector. In this paper we report a lowering in bit rate of about 20% by using the motion vector certainty as background information in the entropy coding/decoding process. We also propose a low- complexity algorithm which does not require motion estimation/compensation, but uses the motion vector certainty.
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