Pixel Blur Research Articles

Multiple-image encryption scheme based on a new 2D hyperchaotic map with blurred pixels

It is known that chaotic, especially hyperchaotic system can be suitable for the application in image encryption owing to itself characteristics. While currently, certain improved chaotic or hyperchaotic systems are confronted with the security issue of encryption due to their less complex dynamical behaviors. To address the problem well, we introduce a novel two-dimensional (2D) crossed hyperchaotic map which is based on the logistic map and the infinite collapse map. The analysis of phase diagram and Lyapunov exponential spectrum demonstrate that the given system can exhibit extensive hyperchaotic behavior and good traversal properties. Moreover, the growing use of digital images has prompted demand for multi-image encryption scheme. For this reason, based on the given 2D crossed hyperchaotic map, a multiple image encryption (MIE) scheme that employs a cross-plane with the operation of simultaneous permutation and diffusion to modify the values of its positions and pixels across multiple images is proposed. A pixel blur preprocessing technique is introduced such that the efficiency of key calculation and the speed and safety of information encryption are greatly improved. Eventually, some simulation examples and security analysis reveal that the put forward encryption scheme is able to keep out kinds of attacks such as the selective plaintext attacks and data loss.

SiameseDenseU-Net-based Semantic Segmentation of Urban Remote Sensing Images

Boundary pixel blur and category imbalance are common problems that occur during semantic segmentation of urban remote sensing images. Inspired by DenseU-Net, this paper proposes a new end-to-end network—SiameseDenseU-Net. First, the network simultaneously uses both true orthophoto (TOP) images and their corresponding normalized digital surface model (nDSM) as the input of the network structure. The deep image features are extracted in parallel by downsampling blocks. Information such as shallow textures and high-level abstract semantic features are fused throughout the connected channels. The features extracted by the two parallel processing chains are then fused. Finally, a softmax layer is used to perform prediction to generate dense label maps. Experiments on the Vaihingen dataset show that SiameseDenseU-Net improves the F1-score by 8.2% and 7.63% compared with the Hourglass-ShapeNetwork (HSN) model and with the U-Net model. Regarding the boundary pixels, when using the same focus loss function based on median frequency balance weighting, compared with the original DenseU-Net, the small-target “car” category F1-score of SiameseDenseU-Net improved by 0.92%. The overall accuracy and the average F1-score also improved to varying degrees. The proposed SiameseDenseU-Net is better at identifying small-target categories and boundary pixels, and it is numerically and visually superior to the contrast model.

Suppression of the color shift of microcavity organic light-emitting diodes through the introduction of a circular polarizer with a nanoporous polymer film

ABSTRACTA circular polarizer (CP) film modified by a nanoporous polymer film (NPF) was applied to improve the viewing angle characteristics of a top-emission organic light-emitting diode (TEOLED) without any serious pixel blur phenomenon. From this approach, the angular color shift (Δu′v′) was significantly improved from 0.096 to 0.056, and the angular luminance distribution was also expanded to the Lambertian distribution direction. The black color had a slightly faded tint, however, like the turn-off condition of the plasma display panel (PDP), regardless of the introduction of NPF below or above the CP. Very interestingly, the surface reflectance of the device with NPF-coated CP was not significantly different from that of the device with non-NPF CP. The pixel blur level according to the distance between the scattering medium and the light source was also investigated. As a result, it was found that the pixel blur phenomenon can be greatly suppressed by applying a thinner encapsulation lid with an NPF film.

Assessing microscope image focus quality with deep learning

BackgroundLarge image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality.ResultsWe present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument.ConclusionsOur deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler.

Light modulation of top emission organic light emitting diodes showing strong microcavity effect by applying multilayered scattering film.

In order to suppress the viewing angle dependence of top emission organic light emitting diodes (TEOEDs) on a strong microcavity structure, we prepared multi-layered nano scattering film which was consisted with transparent planarizing layer and hazy crosslinked scattering layer. Through such an approach, we could obtain not only a stable color shift and luminance distribution with viewing angle but also a negligible pixel blur level. Meanwhile, we investigated a black tint level of TEOLEDs after attachment of circular polarizer (CP) on various nano scattering films because nano scattering film deteriorates a black level. We found that the black level could be improved from the black tint by reducing the refractive index difference between planarizing layer and scattering layer.

Suppression of viewing angle dependence of organic light emitting diodes by introduction of circular polarizer with nanoporous polymer film

To improve poor viewing angle characteristics of top emission organic light emitting diodes (TEOLEDs) without any serious pixel blur phenomena, we applied a circular polarizer (CP) film modified by nanoporous polymer film (NPF). From this approach, we could improve the color shift significantly (Δu'v’ in CIE 1976 with viewing angle, from 0.0165 to 0.0114), and expand angular light emission distribution of off-axis by applying CP with NPF. We found that black color shift was little associated with NPF position change although black color was slightly faded tint by applying NPF, as like plasma display panel(PDP)’s off condition. Very interestingly, we found that the reflectance was not changed seriously whether NPF exists between glass encapsulation and CP or not. Besides, we investigated the pixel blur level when we applied different condition in encapsulation thickness and morphology of diffusing films. As a result, we found that the pixel blur phenomena can be significantly suppressed by applying thin encapsulation and NPF type diffuser film.

Fast depth from defocus from focal stacks

We present a new depth from defocus method based on the assumption that a per pixel blur estimate (related with the circle of confusion), while ambiguous for a single image, behaves in a consistent way when applied over a focal stack of two or more images. This allows us to fit a simple analytical description of the circle of confusion to the different per pixel measures to obtain approximate depth values up to a scale. Our results are comparable to previous work while offering a faster and flexible pipeline.

Light diffusing effects of nano and micro-structures on OLED with microcavity.

We examined the light diffusing effects of nano and micro-structures on microcavity designed OLEDs. The results of FDTD simulations and experiments showed that the pillar shaped nano-structure was more effective than the concave micro-structure for light diffusing of microcavity OLEDs. The sharp luminance distribution of the microcavity OLED was changed to near Lambertian luminance distribution by the nano-structure, and light diffusing effects increased with the height of the nano-structure. Furthermore, the nano-structure has advantages including light extraction of the substrate mode, reproducibility of manufacturing process, and minimizing pixel blur problems in an OLED display panel. The nano-structure is a promising candidate for a light diffuser, resolving the viewing angle problems in microcavity OLEDs.

Multiple-frequency excitation wideband MRI (ME-WMRI).

In this study multiple-frequency excitation wideband MRI (ME-WMRI), a technique designed to acquire simultaneously excited images without blurring was proposed. ME-WMRI consists of (a) a coherent acquisition sequence that applies refocusing gradient during readout to mitigate signal dephasing, and (b) a signal enhancement procedure that further reduces image blurring. This two-step method was tested on phantom imaging andin vivo imaging with up to 3.84 pixel blur. Imaging results in both phantom andin vivo show that the lost details were successfully restored in the blur-mitigated images, and the accelerated images were comparable to standard images. The proposed ME-WMRI method can effectively undo the image blurring, and thus removes the limitation on fast simultaneous multislice MRI systems with a large acceleration factor.

Multiaperture imaging

We study the reconstruction of a high-resolution image from multiple low-resolution images by using a nonlinear iterative backprojection algorithm. We exploit diversities in the imaging channels, namely, the number of imagers, magnification, position, rotation, and fill factor, to undo the degradation caused by the optical blur, pixel blur, and additive noise. We quantify the improvements gained by these diversities in the reconstruction process and discuss the trade-off among system parameters. As an example, for a system in which the pixel size is matched to the diffraction-limited optical blur size at a moderate detector noise level, we can reduce the reconstruction root-mean-square error by 570% by using 16 cameras and a large amount of diversity. The algorithm was implemented on a 56 camera array specifically constructed to demonstrate the resolution-enhancement capabilities. Practical issues associated with building and operating this device are presented and analyzed.