Visibility Restoration for Single Hazy Image Using Dual Prior Knowledge

Single image haze removal has been a challenging task due to its super ill-posed nature. In this paper, we propose a novel single image algorithm that improves the detail and color of such degraded images. More concretely, we redefine a more reliable atmospheric scattering model (ASM) based on our previous work and the atmospheric point spread function (APSF). Further, by taking the haze density spatial feature into consideration, we design a scene-wise APSF kernel prediction mechanism to eliminate the multiple-scattering effect. With the redefined ASM and designed APSF, combined with the existing prior knowledge, the complex dehazing problem can be subtly converted into one-dimensional searching problem, which allows us to directly obtain the scene transmission and thereby recover visually realistic results via the proposed ASM. Experimental results verify that our algorithm outperforms several state-of-the-art dehazing techniques in terms of robustness, effectiveness, and efficiency

Adenoid lymphocyte heterogeneity in pediatric adenoid hypertrophy and obstructive sleep apnea

IntroductionAdenoid hypertrophy is the main cause of obstructive sleep apnea in children. Previous studies have suggested that pathogenic infections and local immune system disorders in the adenoids are associated with adenoid hypertrophy. The abnormalities in the number and function of various lymphocyte subsets in the adenoids may play a role in this association. However, changes in the proportion of lymphocyte subsets in hypertrophic adenoids remain unclear.MethodsTo identify patterns of lymphocyte subsets in hypertrophic adenoids, we used multicolor flow cytometry to analyze the lymphocyte subset composition in two groups of children: the mild to moderate hypertrophy group (n = 10) and the severe hypertrophy group (n = 5).ResultsA significant increase in naïve lymphocytes and a decrease in effector lymphocytes were found in severe hypertrophic adenoids.DiscussionThis finding suggests that abnormal lymphocyte differentiation or migration may contribute to the development of adenoid hypertrophy. Our study provides valuable insights and clues into the immunological mechanism underlying adenoid hypertrophy

A Low-Light Image Enhancement Method Based on Image Degradation Model and Pure Pixel Ratio Prior

Images captured in low-light conditions are prone to suffer from low visibility, which may further degrade the performance of most computational photography and computer vision applications. In this paper, we propose a low-light image degradation model derived from the atmospheric scattering model, which is simple but effective and robust. Then, we present a physically valid image prior named pure pixel ratio prior, which is a statistical regularity of extensive nature clear images. Based on the proposed model and the image prior, a corresponding low-light image enhancement method is also presented. In this method, we first segment the input image into scenes according to the brightness similarity and utilize a high-efficiency scene-based transmission estimation strategy rather than the traditional per-pixel fashion. Next, we refine the rough transmission map, by using a total variation smooth operator, and obtain the enhanced image accordingly. Experiments on a number of challenging nature low-light images verify the effectiveness and robustness of the proposed model, and the corresponding method can show its superiority over several state of the arts

A Single Image Dehazing Method Using Average Saturation Prior

Outdoor images captured in bad weather are prone to yield poor visibility, which is a fatal problem for most computer vision applications. The majority of existing dehazing methods rely on an atmospheric scattering model and therefore share a common limitation; that is, the model is only valid when the atmosphere is homogeneous. In this paper, we propose an improved atmospheric scattering model to overcome this inherent limitation. By adopting the proposed model, a corresponding dehazing method is also presented. In this method, we first create a haze density distribution map of a hazy image, which enables us to segment the hazy image into scenes according to the haze density similarity. Then, in order to improve the atmospheric light estimation accuracy, we define an effective weight assignment function to locate a candidate scene based on the scene segmentation results and therefore avoid most potential errors. Next, we propose a simple but powerful prior named the average saturation prior (ASP), which is a statistic of extensive high-definition outdoor images. Using this prior combined with the improved atmospheric scattering model, we can directly estimate the scene atmospheric scattering coefficient and restore the scene albedo. The experimental results verify that our model is physically valid, and the proposed method outperforms several state-of-the-art single image dehazing methods in terms of both robustness and effectiveness

An Effective and Robust Single Image Dehazing Method Using the Dark Channel Prior

In this paper, we propose a single image dehazing method aiming at addressing the inherent limitations of the extensively employed dark channel prior (DCP). More concretely, we introduce the Gaussian mixture model (GMM) to segment the input hazy image into scenes based on the haze density feature map. With the segmentation results, combined with the proposed sky region detection method, we can effectively recognize the sky region where the DCP cannot well handle this. On the basis of sky region detection, we then present an improved global atmospheric light estimation method to increase the estimation accuracy of the atmospheric light. Further, we present a multi-scale fusion-based strategy to obtain the transmission map based on DCP, which can significantly reduce the blocking artifacts of the transmission map. To further rectify the error-prone transmission within the sky region, an adaptive sky region transmission correction method is also presented. Finally, due to the segmentation-blindness of GMM, we adopt the guided total variation (GTV) to tackle this problem while eliminating the extensive texture details contained in the transmission map. Experimental results verify the power of our method and show its superiority over several state-of-the-art methods

Femtosecond Time-Resolved Observation of Relaxation and Wave Packet Dynamics of the S1 State in Electronically Excited <i>o</i>-Fluoroaniline

Quantum beat frequency is the basis for understanding interference effects and vibrational wave packet dynamics and has important applications. Using femtosecond time-resolved mass spectrometry and femtosecond time-resolved photoelectron image combined with theoretical calculations, we study the electronic excited-state relaxation of o-fluoraniline molecule and the time-dependent evolution of vibrational wave packets between different eigenstates. After the molecule absorbs a photon of 288.3 nm and is excited to the S1 state, intramolecular vibrational redistribution first occurs on the time scale τ1 = 349 fs, and then the transition to the triplet state occurs through the intersystem crossing on the time scale τ2 = 583 ps, and finally, the triplet state occurs decays slowly through the time scale τ3 = 2074 ps. We find the intramolecular vibrational redistribution is caused by the 00, 10b1 and 16a1 vibrational modes of the Sl state origin. That is, the 288.3 nm femtosecond laser excites the molecule to the S1 state, and the continuous flow of the vibrational wave packet prepares a coherent superposition state of three vibrational modes. Through extracting the oscillation of different peak intensities in the photoelectron spectrum, we observe reversible changes caused by mutual interference of the S1 00, S1 10b1 and S1 16a1 states when the wave packets flow. When the pump pulse is 280 nm, the beat frequency disappears completely. This is explained in terms of increases in the vibrational field density and characteristic period of oscillation, and statistical averaging makes the quantum effect smooth and indistinguishable. In addition, the Rydberg component of the S1 state is more clearly resolved by combining experiment and theory

Deep learning enabled topological design of exceptional points for multi-optical-parameter control

Abstract Metasurfaces are 2D artificial nanostructures that exhibit fascinating optical phenomena and flexible capabilities. Multi-optical-parameter metasurfaces have advantages over single-function or single-dimensional metasurfaces, especially in practical applications like holography, sub-diffraction imaging, and vectorial fields. However, achieving multi-optical-parameter control is challenging due to a lack of design strategy, limited manipulation channels, and signal-to-noise ratio problems. Exceptional points (EPs) possess inherent polarization decoupling properties and allow for amplitude and wavelength modulation, opening up research prospects for multi-optical-parameter electromagnetic field modulation and developing compact integrated devices. Leveraging deep learning, we observe topological charge conservation and utilize the topologically protected optical parameter distribution around scattered EPs. Based on these, we introduce amplitude-phase multiplexing and wavelength division multiplexing devices. Our work allows rapid and precise discovery of EPs topology, offers a powerful tool for digging related physics, and provides a paradigm for multi-optical parametric manipulation with high performance and less crosstalk, which is critical for imaging, encryption, and information storage applications