Modeling and Compensating Temperature-Dependent Non-Uniformity Noise in IR Microbolometer Cameras

Images rendered by uncooled microbolometer-based infrared (IR) cameras are severely degraded by the spatial non-uniformity (NU) noise. The NU noise imposes a fixed-pattern over the true images, and the intensity of the pattern changes with time due to the temperature instability of such cameras. In this paper, we present a novel model and a compensation algorithm for the spatial NU noise and its temperature-dependent variations. The model separates the NU noise into two components: a constant term, which corresponds to a set of NU parameters determining the spatial structure of the noise, and a dynamic term, which scales linearly with the fluctuations of the temperature surrounding the array of microbolometers. We use a black-body radiator and samples of the temperature surrounding the IR array to offline characterize both the constant and the temperature-dependent NU noise parameters. Next, the temperature-dependent variations are estimated online using both a spatially uniform Hammerstein-Wiener estimator and a pixelwise least mean squares (LMS) estimator. We compensate for the NU noise in IR images from two long-wave IR cameras. Results show an excellent NU correction performance and a root mean square error of less than 0.25 ∘C, when the array’s temperature varies by approximately 15 ∘C.

Quasi noise-free digital holography

One of the main drawbacks of Digital Holography (DH) is the coherent nature of the light source, which severely corrupts the quality of holographic reconstructions. Although numerous techniques to reduce noise in DH have provided good results, holographic noise suppression remains a challenging task. We propose a novel framework that combines the concepts of encoding multiple uncorrelated digital holograms, block grouping and collaborative filtering to achieve quasi noise-free DH reconstructions. The optimized joint action of these different image-denoising methods permits the removal of up to 98% of the noise while preserving the image contrast. The resulting quality of the hologram reconstructions is comparable to the quality achievable with non-coherent techniques and far beyond the current state of art in DH. Experimental validation is provided for both single-wavelength and multi-wavelength DH, and a comparison with the most used holographic denoising methods is performed.

Spectral Object Recognition in Hyperspectral Holography with Complex-Domain Denoising

In this paper, we have applied a recently developed complex-domain hyperspectral denoiser for the object recognition task, which is performed by the correlation analysis of investigated objects’ spectra with the fingerprint spectra from the same object. Extensive experiments carried out on noisy data from digital hyperspectral holography demonstrate a significant enhancement of the recognition accuracy of signals masked by noise, when the advanced noise suppression is applied.

A novel crystal-analyzer phase retrieval algorithm and its noise property

A description of the rocking curve in diffraction enhanced imaging (DEI) is presented in terms of the angular signal response function and a simple multi-information retrieval algorithm based on the cosine function fitting. A comprehensive analysis of noise properties of DEI is also given considering the noise transfer characteristic of the X-ray source. The validation has been performed with synchrotron radiation experimental data and Monte Carlo simulations based on the Geant4 toolkit combined with the refractive process of X-rays, which show good agreement with each other. Moreover, results indicate that the signal-to-noise ratios of the refraction and scattering images are about one order of magnitude better than that of the absorption image at the edges of low-Z samples. The noise penalty is drastically reduced with the increasing photon flux and visibility. Finally, this work demonstrates that the analytical method can build an interesting connection between DEI and GDPCI (grating-based differential phase contrast imaging) and is widely suitable for a variety of measurement noise in the angular signal response imaging prototype. The analysis significantly contributes to the understanding of noise characteristics of DEI images and may allow improvements to the signal-to-noise ratio in biomedical and material science imaging.