Realistic camera noise modeling with application to improved HDR synthesis

Abstract Due to the ongoing miniaturization of digital camera sensors and the steady increase of the “number of megapixels”, individual sensor elements of the camera become more sensitive to noise, even deteriorating the final image quality. To go around this problem, sophisticated processing algorithms in the devices, can help to maximally exploit the knowledge on the sensor characteristics (e.g., in terms of noise), and offer a better image reconstruction. Although a lot of research focuses on rather simplistic noise models, such as stationary additive white Gaussian noise (AWGN), only limited attention has gone to more realistic digital camera noise models. In this paper, we first present a digital camera noise model that takes several processing steps in the camera into account, such as sensor signal amplification, clipping, post-processing, ... We then apply this noise model to the reconstruction problem of high dynamic range (HDR) images from a small set of low dynamic range exposures of a static scene. In literature, HDR reconstruction is mostly performed by computing a weighted average, in which the weights are directly related to the observer pixel intensities of the LDR image. In this work, we derive a Bayesian probabilistic formulation of a weighting function that is near-optimal in the MSE sense (or SNR sense) of the reconstructed HDR image, by assuming exponentially distributed irradiance values. We define the weighting function as the probability that the observed pixel intensity is approximately unbiased. The weighting function can be directly computed based on the noise model parameters, which gives rise to different symmetric and asymmetric shapes when electronic noise or photon noise is dominant. We also explain how to deal with the case that some of the noise model parameters are unknown and explain how the camera response function can be estimated using the presented noise model. Finally, experimental results are provided to support our findings

Editorial to Special Issue “Remote Sensing Image Denoising, Restoration and Reconstruction”

publishedVersionNon peer reviewe

Advanced statistical tools for enhanced quality digital imaging with realistic capture models

AbstractDummy.*Peer Reviewe

Efektivno smanjenje koncentracije fosfornih jedinjenja u gradskim otpadnim vodama u Subotici izmedju 2010. i 2018. godine

Prečišćavanje gradskih otpadnih voda u Subotici vrši se više od četiri decenije. Od 2010. godine osavremenjena je tehnologija biološkog prečišćavanja otpadnih voda u cilju otklanjanja nutrijenata. Efektivno se smanjuje koncentracija fosfora, koji po ekološkim načelima predstavlja jedan od najbitnijih eutrofikacijskih faktora opterećenja vodnog tela recipijenta. U radu će se analizirati kretanje koncentracije fosfora od ulaznih (influent) do izlaznih parametara (effluent) u preseku procenta uklanjanja

Journal of Electronic Imaging 15(2), 023008 (Apr–Jun 2006) Fuzzy logic recursive motion detection and denoising of video sequences

Abstract. We propose a fuzzy logic recursive scheme for motion detection and spatiotemporal filtering that can deal with the Gaussian noise and unsteady illumination conditions in both the temporal and spatial directions. Our focus is on applications concerning tracking and denoising of image sequences. We process an input noisy sequence with fuzzy logic motion detection to determine the degree of motion confidence. The proposed motion detector combines the membership of the temporal intensity changes, appropriately using fuzzy rules, where the membership degree of motion for each pixel in a 2-D sliding window is determined by a proposed membership function. Both the fuzzy membership function and the fuzzy rules are defined in such a way that the performance of the motion detector is optimized in terms of its robustness to noise and unsteady lighting conditions. We simultaneously perform tracking and recursive adaptive temporal filtering, where the amount of filtering is inversely proportional to the confidence in the existence of motion. Finally, temporally filtered frames are further processed by a proposed spatial filter to obtain a denoised image sequence. Our main contribution is a robust novel fuzzy recursive scheme for motion detection and temporal filtering. We evaluate the proposed motion detection algorithm using two criteria: (1) robustness to noise and to changing illumination conditions and (2) motion blur in temporal recursive denoising. Additionally, we make comparisons in terms of noise reduction with other state of the art video denoising techniques. © 2006 SPIE and IS&T. �DOI: 10.1117/1.2201548�