Simple Gaussian Filter Research Articles

A Bispectral Approach for Destriping and Denoising the Sea Surface Temperature from SGLI Thermal Infrared Data

Abstract Stripe noise is a common issue in sea surface temperatures (SSTs) retrieved from thermal infrared data obtained by satellite-based multidetector radiometers. We developed a bispectral filter (BSF) to reduce the stripe noise. The BSF is a Gaussian filter and an optimal estimation method for the differences between the data obtained at the split window. A kernel function based on the physical processes of radiative transfer has made it possible to reduce stripe and random noise in retrieved SSTs without degrading the spatial resolution or generating bias. The Second-Generation Global Imager (SGLI) is an optical sensor on board the Global Change Observation Mission–Climate (GCOM-C) satellite. We applied the BSF to SGLI data and validated the retrieved SSTs. The validation results demonstrate the effectiveness of BSF, which reduced stripe noise in the retrieved SGLI SSTs without blurring SST fronts. It also improved the accuracy of the SSTs by about 0.04 K (about 13%) in the robust standard deviation. Significance Statement This method reduces stripe noise and improves the accuracy of SST data with minimal compromise of spatial resolution. The method assumes the relationship between the brightness temperature and the brightness temperature difference in the split window based on the physical background of atmospheric radiative transfer. The physical background of the data provides an easy solution to a complex problem. Although destriping generally requires a complex algorithm, our approach is based on a simple Gaussian filter and is easy to implement.

Derivative-based band clustering and multi-agent PSO optimization for optimal band selection of hyper-spectral images

Images (HSIs) are popular in diversified applications, such as geosciences, biomedical imaging, molecular biology, agriculture, astronomy, food quality and safety assessment, surveillance and physics-related research. The rich spatial and spectral information of HSI is the key factors for robust representation of class-specific objects, in remote sensing applications. But, these images often suffer from Hughes effect. This effect causes the recording of information about a single scene in multiple spectral bands. This demands a dimensionality reduction step, which can either be a feature reduction/extraction or a feature selection. The feature selection process is commonly called band selection (BS) in the HS data set. The current study proposes an unsupervised BS technique, which is accomplished in three steps, including preprocessing of spectral bands, adjacent band clustering, and multi-agent optimization. Spatio-spectral (using a simple Gaussian filter) information is extracted to evaluate the performance using SVM classifier with different state-of-the-art band selection approaches. The performance of the proposed approach is evaluated for metrics including overall accuracy (OA), average accuracy (AA) and Kappa ( $$\kappa$$ ). The experimental results are promising as these surpass that of other approaches.

Comparing methods for analysing time scale dependent correlations in irregularly sampled time series data

Time series derived from paleoclimate archives are often irregularly sampled in time and thus not analysable using standard statistical methods such as correlation analyses. Although measures for the similarity between time series have been proposed for irregular time series, they do not account for the time scale dependency of the relationship. Stochastically distributed temporal sampling irregularities act qualitatively as a low-pass filter reducing the influence of fast variations from frequencies higher than about 0.5 (Δtmax)−1, where Δtmax is the maximum time interval between observations. This may lead to overestimated correlations if the true correlation increases with time scale. Typically, correlations are underestimated due to a non-simultaneous sampling of time series.Here, we investigated different techniques to estimate time scale dependent correlations of weakly irregularly sampled time series, with a particular focus on different resampling methods and filters of varying complexity. The methods were tested on ensembles of synthetic time series that mimic the characteristics of Holocene marine sediment temperature proxy records. We found that a linear interpolation of the irregular time series onto a regular grid, followed by a simple Gaussian filter was the best approach to deal with the irregularity and account for the time scale dependence. This approach had both, minimal filter artefacts, particularly on short time scales, and a minimal loss of information due to filter length.

Infrared and visible image fusion using co-occurrence filter

In this paper, an effective image fusion method for infrared image and visible image is proposed for generating a high-quality fused image to deal with the issue that existing image fusion methods suffer from loss of tiny details. The major contributions are as follows: (1) We apply the Co-occurrence filter (CoF), a recently proposed edge-preserving technique, to image fusion and propose a CoF-based image fusion framework to merge tiny details of the multiple input images. The fusion processing is respectively performed on the base layer and the detail layer, which are decomposed by the simple gaussian filter. (2) We propose a novel strategy to fuse the base layers and detail layers. The CoF is adopted directly to fuse the detail layer and an iterative CoF is used to fuse the base layer. It is demonstrated through experimental results and evaluations that the proposed method outperforms the state-of-the-art fusion methods with respect to edge preserving by both subjective evaluation and objective assessment.

Abstract ID: 195 A fast Cone-Beam CT scatter correction method with Monte Carlo simulation using GPUMCD

GPUMCD, a fast GPU-based Monte Carlo transport code, was initially developed and validated for photon and electron dose calculations in radiation oncology. In this work, the code was adapted to rapidly simulate realistic X-ray Cone-Beam Computed Tomography (CBCT) projections, and in particular to estimate the scatter component of the signal. This estimate is thereafter filtered and used for scatter correction, which is especially important in a CBCT geometry. The signal generated by scattered photons on a 1028 × 768 detector was estimated with GPUMCD. The simulation geometry was derived from actual CBCT reconstructions (pelvis, thorax), with density and materials assigned to each voxel with a HU-electronic density calibration curve. Simulations were conducted on a NVIDIA GTX970m GPU with either 10 7 or 10 9 photons per projection. Recursive Gaussian filtering in projections with 10 7 photons was performed to assess the potential of faster simulations conducted with fewer photons. A simple Gaussian filter was used for the projection of 10 9 photons. The primary signal was obtained with deterministic ray-tracing, and normalized by the primary signal computed derived from the Monte Carlo simulation. Simulated projections with 10 7 photons were obtained in 3.5 s while the primary signal estimation took 0.2s. Recursive Gaussian filtering of projections was shown to be an effective way to reduce the simulation time by a factor 100 and preserve more complex scatter patterns. The simulation times reported here let envision a Monte Carlo-based scatter correction strategies in CBCT reconstruction.

Effect of species and grinding disc distance on the surface roughness parameters of medium density fiberboard

Surface quality of medium density fiberboard (MDF), as a very important criterion for further finishing and utilization, was evaluated using a wide range of measuring parameters (instruments, filters, filtering cut-off length, measuring length, measuring resolution, roughness parameters). However, these various approaches make any data comparison rather difficult. Furthermore, filtering the measured data with a simple Gaussian filter has proved unreliable for wood surfaces. A new, more robust approach by using a Robust Gaussian Regression filter together with a previously tested set of measuring and evaluating parameters was used in this paper in order to quantify the surface roughness of MDF manufactured from different species (beech, poplar, birch, Scots pine, mixture 50 % Scots pine and 50 % beech) and using three different grinding disc distances (0.06, 0.15 and 0.6 mm). If manufacturing parameters are kept constant, the processing roughness of MDF is similar, but variable species anatomy and fuzziness effects will give differentiation in total roughness. The results indicated that fibers made of a mixture of wood chips from Scots pine and beech led to the smoothest MDF surface, followed closely by poplar, birch, and Scots pine. The roughest surface was measured for the panels made of beech fibers. Investigating the influence of the grinding disc distance, the roughest MDF surface was obtained when a grinding gap of 0.6 mm was used. However, surface roughness was found to be quite similar applying a grinding disc distance of 0.06 mm slightly lower for 0.15 mm. This finding indicates that a too fine grinding gap does not improve the MDF surface roughness.

Fast single image dehazing based on image fusion

Images captured in foggy weather conditions often fade the colors and reduce the contrast of the observed objects. An efficient image fusion method is proposed to remove haze from a single input image. First, the initial medium transmission is estimated based on the dark channel prior. Second, the method adopts an assumption that the degradation level affected by haze of each region is the same, which is similar to the Retinex theory, and uses a simple Gaussian filter to get the coarse medium transmission. Then, pixel-level fusion is achieved between the initial medium transmission and coarse medium transmission. The proposed method can recover a high-quality haze-free image based on the physical model, and the complexity of the proposed method is only a linear function of the number of input image pixels. Experimental results demonstrate that the proposed method can allow a very fast implementation and achieve better restoration for visibility and color fidelity compared to some state-of-the-art methods.

A feature-based model of symmetry detection.

Symmetry detection is important for many biological visual systems, including those of mammals, insects and birds. We constructed a symmetry-detection algorithm with two stages: location of the visually salient features of the image, then evaluating the symmetry of these features over a long range, by means of a simple Gaussian filter. The algorithm detects the axis of maximum symmetry for human faces (or any arbitrary image) and calculates the magnitude of the asymmetry. We have evaluated the algorithm on the dataset of Rhodes et al. (1998 Psychonom. Bull. Rev. 5, 659-669) and found that the algorithm is able to discriminate small variations of symmetry created by computer-manipulating the symmetry levels in individual faces, and that the values measured by the algorithm correlate well with human psycho-physical symmetry ratings.