Efficient Color Correction Using Normalized Singular Value for Duststorm Image Enhancement

A duststorm image has a reddish or yellowish color cast. Though a duststorm image and a hazy image are obtained using the same process, a hazy image has no color distortion as it has not been disturbed by particles, but a duststorm image has color distortion owing to an imbalance in the color channel, which is disturbed by sand particles. As a result, a duststorm image has a degraded color channel, which is rare in certain channels. Therefore, a color balance step is needed to enhance a duststorm image naturally. This study goes through two steps to improve a duststorm image. The first is a color balance step using singular value decomposition (SVD). The singular value shows the image’s diversity features such as contrast. A duststorm image has a distorted color channel and it has a different singular value on each color channel. In a low-contrast image, the singular value is low and vice versa. Therefore, if using the channel’s singular value, the color channels can be balanced. Because the color balanced image has a similar feature to the haze image, a dehazing step is needed to improve the balanced image. In general, the dark channel prior (DCP) is frequently applied in the dehazing step. However, the existing DCP method has a halo effect similar to an over-enhanced image due to a dark channel and a patch image. According to this point, this study proposes to adjustable DCP (ADCP). In the experiment results, the proposed method was superior to state-of-the-art methods both subjectively and objectively.

Phase Retrieval Based on Deep Learning in Grating Interferometer

Grating interferometry is a promising technique to obtain differential phase contrast images with illumination source of low intrinsic transverse coherence. However, retrieving the phase contrast image from the differential phase contrast image is difficult due to the accumulated noise and artifacts from the differential phase contrast image (DPCI) reconstruction. In this paper, we implemented a deep learning-based phase retrieval method to suppress these artifacts. Conventional deep learning based denoising requires noisy-clean image pair, but it is not feasible to obtain sufficient number of clean images for grating interferometry. In this paper, we apply a recently developed neural network called Noise2Noise (N2N) that uses noise-noise image pairs for training. We obtained many differential phase contrast images through combination of phase stepping images, and these were used as noise input/target pairs for N2N training. The application of the N2N network to simulated and measured DPCI showed that the phase contrast images were retrieved with strongly suppressed phase retrieval artifacts. These results can be used in grating interferometer applications which uses phase stepping method.

Image Quality Evaluation of a Digital Radiography System Made in Thailand

Background. The National Science and Technology Development Agency (NSTDA) in Thailand researched and prototyped digital radiography systems under the brand name BodiiRay aiming for sustainable development and affordability of medical imaging technology. The image restoration and enhancement were implemented for the systems. Purpose. The image quality of the systems was evaluated using images from phantoms and from healthy volunteers. Methods. The survey phantom images from BodiiRay and other two commercial systems using the exposure settings for the chest, the abdomen, and the extremity were evaluated by three experience observers in terms of the high-contrast image resolution, the low-contrast image detectability, and the grayscale differentiation. The volunteer images of the chests, the abdomens, and the extremities from BodiiRay were evaluated by three specialized radiologists based on visual grading on 5-point scaled questionnaires for the anatomy visibility, the image quality satisfaction, and the diagnosis confidence in using the images. Results. BodiiRay phantom results were similar to those from the commercial systems. The overall performance averaged across the exposure settings showed that BodiiRay was slightly better than Fujifilm FDR Go in the low-contrast detectability ( p = 0.033 ) and in the grayscale differentiation ( p = 0.004 ). It was also slightly better than Siemens YSIO Max in the high-contrast resolution ( p = 0.018 ). The images of chest, pelvis, and hand phantoms illustrated comparable visual quality. For volunteer images, the percentage of the images scored ≥4 ranged from 61% to 99%, 23% to 92%, and 96% to 99% for the chest, abdomen, and extremity images, respectively. The average score ranged from 3.63 to 4.46, 3.18 to 4.21, and 4.41 to 4.51 for the chest, abdomen, and extremity images, respectively. Conclusion. The phantom image results showed the comparability of these systems. The clinical evaluation showed BodiiRay images provided sufficient image qualities for digital radiography of these body parts.

How Good is Too Good? A Subjective Study on Over Enhancement of Images

For a long time different studies have focused on introducing new image enhancement techniques. While these techniques show a good performance and are able to increase the quality of images, little attention has been paid to how and when overenhancement occurs in the image. This could possibly be linked to the fact that current image quality metrics are not able to accurately evaluate the quality of enhanced images. In this study we introduce the Subjective Enhanced Image Dataset (SEID) in which 15 observers are asked to enhance the quality of 30 reference images which are shown to them once at a low and another time at a high contrast. Observers were instructed to enhance the quality of the images to the point that any more enhancement will result in a drop in the image quality. Results show that there is an agreement between observers on when over-enhancement occurs and this point is closely similar no matter if the high contrast or the low contrast image is enhanced.

Flexible STEM with Simultaneous Phase and Depth Contrast

Recent advances in scanning transmission electron microscopy (STEM) have rekindled interest in multi-channel detectors and prompted the exploration of unconventional scan patterns. These emerging needs are not yet addressed by standard commercial hardware. The system described here incorporates a flexible scan generator that enables exploration of low-acceleration scan patterns, while data are recorded by a scalable eight-channel array of nonmultiplexed analog-to-digital converters. System integration with SerialEM provides a flexible route for automated acquisition protocols including tomography. Using a solid-state quadrant detector with additional annular rings, we explore the generation and detection of various STEM contrast modes. Through-focus bright-field scans relate to phase contrast, similarly to wide-field TEM. More strikingly, comparing images acquired from different off-axis detector elements reveals lateral shifts dependent on defocus. Compensation of this parallax effect leads to decomposition of integrated differential phase contrast (iDPC) to separable contributions relating to projected electric potential and to defocus. Thus, a single scan provides both a computationally refocused phase contrast image and a second image in which the signed intensity, bright or dark, represents the degree of defocus.

A Spatial Compounding Method for Non-Delayed Sequential Beamforming

We present a new spatial compounding method to improve the contrast of ultrasonic images for non-delayed sequential beamforming (NDSB). Sequential beamforming adopts more than one beamformer to reconstruct B-mode images which has the advantage of simple front-end electronics and fast data transfer rate. Via field pattern analysis, we propose a compounding method where two more sub-images can be reconstructed along with the NDSB sub-image. These sub-images can be seen as being produced with different transmit origins; thus, their summation enhances image contrast. Image quality was analyzed in terms of spatial resolution, contrast ratio (CR), and contrast-to-noise ratio (CNR). The proposed compounding method improves the lateral resolution up to 41%. In vitro results confirm a 13.0-dB CR and 4.0-dB CNR improvement. In vivo results reveal 10.9-dB and 6.0-dB improvement in CR and CNR for cross-section jugular vein and 8.0-dB and 4.5-dB improvement in CR and CNR for the longitudinal-section carotid artery.