Adaptive Linear Interpolation Research Articles

An Adaptive Colour Calibration for Crowdsourced Images in Heritage Preservation Science

In this paper, an adaptive image calibration technique suitable for cultural heritage surveillance in controlled environments is proposed. Heritage pieces, as objects important to conserve, are prone to degradation and thus in the need of a constant watch in order to assess the moment where maintenance is required, preferably in a non-invasive way. By means of a crowdsourcing approach, we take as surveillance cues the pictures visitors take of the piece under surveillance to avoid the need of a permanent human effort on monitoring. In this paper we propose a novel image calibration approach destined to monitor the state of a cultural heritage piece taking as information cues photographs of different sources. Our technique aims to surpass the colorimetry difference between pictures taken by different cameras, performing an adaptive linear interpolation having a given display with a set of colours as reference values, thus transforming the initial colour space of any picture to a fixed one. This is performed in the RGB domain. Lastly, the pictures are transformed to the CIELAB space to extract the colour coordinates and measure the overall objective quality of the calibration. Acting like this allows the heritage monitoring of its possible degradation along time in an effective way without incurring in expensive resources.

Effects of defect pixel correction algorithms for x-ray detectors on image quality in planar projection and volumetric CT data sets

In this study we compared various defect pixel correction methods for reducing artifact appearance within projection images used for computed tomography (CT) reconstructions.Defect pixel correction algorithms were examined with respect to their artifact behaviour within planar projection images as well as in volumetric CT reconstructions. We investigated four algorithms: nearest neighbour, linear and adaptive linear interpolation, and a frequency-selective spectral-domain approach.To characterise the quality of each algorithm in planar image data, we inserted line defects of varying widths and orientations into images. The structure preservation of each algorithm was analysed by corrupting and correcting the image of a slit phantom pattern and by evaluating its line spread function (LSF). The noise preservation was assessed by interpolating corrupted flat images and estimating the noise power spectrum (NPS) of the interpolated region.For the volumetric investigations, we examined the structure and noise preservation within a structured aluminium foam, a mid-contrast cone-beam phantom and a homogeneous Polyurethane (PUR) cylinder.The frequency-selective algorithm showed the best structure and noise preservation for planar data of the correction methods tested. For volumetric data it still showed the best noise preservation, whereas the structure preservation was outperformed by the linear interpolation.The frequency-selective spectral-domain approach in the correction of line defects is recommended for planar image data, but its abilities within high-contrast volumes are restricted. In that case, the application of a simple linear interpolation might be the better choice to correct line defects within projection images used for CT.

An Efficient Linearization Scheme for a Digital Polar EDGE Transmitter

A new linearization scheme is proposed, which compensates for nonlinear distortions experienced in the amplitude-modulation path of a digital polar EDGE transmitter integrated in a 65-nm CMOS transceiver system-on-chip (SoC) based on the Digital RF Processor (DRP) technology. The measured amplitude and phase distortions are stored in lookup tables and used for predistortion without requiring inversion computations, thus achieving significant complexity reduction. Adaptive linear interpolation along with adaptive resolution enhancement provides the desired performance across power levels. With the presented scheme, the transmitter's measured performance significantly exceeds the EDGE specifications with an error vector magnitude (EVM) of typically 3% and a close-in modulated spectrum of -64 dB at a 400-kHz offset from the carrier frequency.

Automatic Tortuosity-Based Retinopathy of Prematurity Screening System

Retinopathy of Prematurity (ROP) is an infant disease characterized by increased dilation and tortuosity of the retinal blood vessels. Automatic tortuosity evaluation from retinal digital images is very useful to facilitate an ophthalmologist in the ROP screening and to prevent childhood blindness. This paper proposes a method to automatically classify the image into tortuous and non-tortuous. The process imitates expert ophthalmologists' screening by searching for clearly tortuous vessel segments. First, a skeleton of the retinal blood vessels is extracted from the original infant retinal image using a series of morphological operators. Next, we propose to partition the blood vessels recursively using an adaptive linear interpolation scheme. Finally, the tortuosity is calculated based on the curvature of the resulting vessel segments. The retinal images are then classified into two classes using segments characterized by the highest tortuosity. For an optimal set of training parameters the prediction is as high as 100%.

Comparison of adaptive linear interpolation and conventional linear interpolation for digital radiography systems

Some large field digital radiography systems are currently under development using multiple detectors. These small size two dimension detectors are abutted together to cover a large field. Physical gaps existing between adjacent detectors produce seams between the resultant subimages. In this paper, an adaptive linear interpolation algorithm was introduced for estimating missing information at the seams and was compared with conventional linear interpolation and with nonlinear interpolation. The effectiveness of the algorithms was evaluated for relative/absolute errors. Phantom images were acquired using prototype digital radiography systems. Seams with width ranging from two pixel to six pixel were introduced and adaptive linear interpolation algorithms were applied to estimate missing information at the seams. Quantitatively, the adaptive interpolation offers at least equivalent or less error than that of the linear interpolation algorithm. The experimental results prove that there is a significant difference between two algorithms for tested radiographic images. The comparison results also show that the adaptive interpolation offers better performance than nonlinear interpolation. When developing large field digital radiography imaging systems, gaps between adjacent detectors should be minimized. For narrow seams, the adaptive linear interpolation algorithm is a practical solution because of its simplicity and effectiveness.

Adaptive image interpolation for full-field digital x-ray mammography

Some full-field digital x-ray mammography systems use multiple abutted two-dimensional detectors. Gaps between adjacent detectors produce seams in the composite image. We develop an adaptive linear interpolation method for estimation of missing data in the seams. In this method, the interpolation path is determined on the basis of the similarity between adjacent subimage boundary profiles. In experiments with phantom images and clinical mammograms, the adaptive method performed significantly better than the conventional linear interpolation.