Search

Air-coupled ultrasound (ACU) is increasingly used for nondestructive testing (NDT). With ACU, no contact or coupling agent (e.g., water and ultrasound gel) is needed between transducers and test sample, which provides high measurement reproducibility. However, for testing in production, a minimum separation is often necessary between the sample and the transducers to avoid contamination or transducer damage. Due to wave diffraction, the collimation of the ultrasound beam decreases for larger propagation distances, and ACU images become blurred and show lower defect lateral resolution with increasing sample-transducer separation. This is especially critical to thick composites, where large-size planar sources are used to bridge the large ACU transmission loss with good collimation. In this work, ACU reradiation in unbounded media is extended to NDT of multilayered composites. The extended method is named ACU time reversal (ACU-TR) and significantly improves the defect resolution of ACU imaging. With ACU-TR, the complete pressure distribution radiated by large ACU source is measured with point receivers (RXs) in one plane arbitrarily separated from the sample. By applying acoustic holography physics, it is then possible to quantitatively reconstruct the pressure field directly at arbitrary sample defect planes, which compensates for undesired diffraction phenomena and improves minimum detectable defect size, thereby achieving subwavelength lateral resolution. We tested the method on complex wood-based composite samples based on the ACU far-field measurements at a separation of 160 mm between the sample and the RX transducer. With the proposed method, it is possible to detect surface defects as well as inner defects within composite boards. In the future, by using point RX arrays instead of a scanned microphone, both data acquisition and evaluation can be potentially implemented in real time.

Existing image-difference measures show excellent accuracy in predicting distortions, such as lossy compression, noise, and blur. Their performance on certain other distortions could be improved; one example of this is gamut mapping. This is partly because they either do not interpret chromatic information correctly or they ignore it entirely. We present an image-difference framework that comprises image normalization, feature extraction, and feature combination. Based on this framework, we create image-difference measures by selecting specific implementations for each of the steps. Particular emphasis is placed on using color information to improve the assessment of gamut-mapped images. Our best image-difference measure shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures.

We show that conjoint analysis, a popular multi-attribute preference assessment technique used in market research, is a well suited tool to evaluate a multitude of gamut mapping algorithms simultaneously. Our analysis is based on data from psycho-visual tests assessed in a laboratory and in a web environment. Conjoint analysis allows us to quantify the contribution of every single parameter value to the perceived value of the algorithm; it also allows us to test the influence of additional parameters like gamut size or color shifts. We show that conjoint analysis can be individualized to images or observers if enough data is available. Especially promising in this respect is the combination of individual and population data.

We explore the potential of image-dependent gamut mapping as a constrained optimization problem. The performance of our new approach is compared to standard reference gamut mapping algorithms in psycho-visual tests.

Our topic is the potential of combining global gamut mapping with spatial methods to retain the percepted local image information in gamut mapping algorithms. The main goal is to recover the original local contrast between neighboring pixels in addition to the usual optimization of preserving lightness, saturation, and global contrast. Special emphasis is placed on avoiding artifacts introduced by the gamut mapping algorithm itself. We present an unsharp masking technique based on an edge-preserving smoothing algorithm allowing to avoid halo artifacts. The good performance of the presented approach is verified by a psycho-visual experiment using newspaper printing as a representative of a small destination gamut application. Furthermore, the improved mapping properties are documented with local mapping histograms.