The atmospheric scattering and absorption gives rise to the natural phenomenon of haze, which severely affects the visibility of scenery. Thus, the image taken by the camera can easily lead to over brightness and ambiguity. To resolve an ill-posed and intractable problem of single image dehazing, we propose a straightforward but remarkable prior-atmospheric illumination prior in this paper. The extensive statistical experiments for different colorspaces and theoretical analyses indicate that the atmospheric illumination in hazy weather mainly has a great influence on the luminance channel in YCrCb colorspace, and has less impact on the chrominance channels. According to this prior, we try to maintain the intrinsic color of hazy scene and enhance its visual contrast. To this end, we apply the multiscale convolutional networks that can automatically identify hazy regions and restore deficient texture information. Compared with previous methods, the deep CNNs not only achieve an end-to-end trainable model, but also accomplish an easy image-to-image system architecture. The extensive comparisons and analyses with existing approaches demonstrate that the proposed approach achieves the state-of-the-art performance on several dehazing effects.

Influenced by glowing effects, nighttime haze removal is a challenging ill-posed task. Existing nighttime dehazing methods usually result in glowing artifacts, color shifts, overexposure, and noise amplification. Thus, through statistical and theoretical analyses, we propose a simple and effective gray haze-line prior (GHLP) to identify accurate hazy feature areas. This prior demonstrates that haze is concentrated on the haze line in the RGB color space and can be accurately projected into the gray component in the Y channel of the YUV color space. Based on this prior, we establish a new unified nighttime haze removal framework and then decompose a nighttime hazy image into color and gray components in the YUV color space. Glowing color correction and haze removal are two important consecutive steps in the nighttime dehazing process. The glowing color correction method is designed to separately remove glow in the color component and enhance illumination in the gray component. After obtaining a refined nighttime hazy image, we propose a new structure-aware variational framework to simultaneously estimate the inverted scene radiance and the transmission in the gray component. This approach can not only recover the high-quality nighttime scene radiance but also preserve the significant structural information and intrinsic color of the scene. Quantitative and qualitative comparisons validate the excellent effectiveness of the proposed nighttime dehazing method against previous state-of-the-art methods. In addition, the proposed approach can be extended to achieve image enhancement for inclement weather scenes, such as sandstorm scenes and extreme daytime hazy scenes.

Due to the atmospheric scattering and absorption, hazy weather often occurs in our everyday life, thus reducing the visibility of scenes. Single image dehazing is considered as an ill-posed and challenging problem in computer vision. To restore visibility in inclement weather, we propose an attention-to-attention generative adversarial network (AAGAN) whose motivation is the human visual perceptual mechanism. More specifically, a dense channel attention model is embedded into the encoder. Moreover, its output is projected forward to the corresponding multiscale spatial attention model in the decoder. Both attention models form an attention-to-attention mechanism to implement attention projection, thus capturing global feature dependencies of the whole network. Besides, we analyze the dehazing mechanism based on the atmospheric scattering model, and then utilize an improved RaLSGAN to recover more realistic texture information and enhance visual contrast for different hazy scenes. Finally, in order to improve the visual performance of image restoration, we remove all the instance normalization layers to avoid unnecessary artifacts, and then introduce spectral normalization for all the convolution layers to stabilize the entire training process. Qualitative assessments and analyses demonstrate that our proposed approach can achieve remarkable dehazing performance on both synthetic and real-world scenes against previous state-of-the-art methods.

Chitinozoans are organic-walled microfossils widely recorded in Ordovician to Devonian (ca485–359 Mya) marine sediments and extensively used in high-resolution biostratigraphy. Their biological affinity remains unknown, but most commonly, they are interpreted as eggs of marine metazoans. Here, we provide new insights into their palaeobiology from three lines of inquiry. We examine morphological variation of a new, well-preserved Late Ordovician species,Hercochitina violana; analyse a compiled dataset of measurements on 378 species representing all known chitinozoan genera; and compare these data with the size variation of eggs of both extinct and extant aquatic invertebrates. The results indicate that the magnitude of size variation within chitinozoan species is larger than observed in fossil and modern eggs. Additionally, delicate morphological structures of chitinozoans, such as prosome and complex ornamentation, are inconsistent with the egg hypothesis. Distinct and continuous morphological variation inH.violanais more plausibly interpreted as an ontogenetic series of individual microorganisms, rather than as eggs.

This study used electroencephalogram measurements to investigate the effects of aging on oscillatory theta modulation during an audiovisual discrimination task. By a wavelet-based time–frequency analysis, age-related theta oscillation response differences were observed within a relatively restricted time range (0–500 ms) over frontal–central regions. Older adults showed stronger theta spectral power during visual and audiovisual stimuli in the left frontal regions; however, young adults showed stronger theta spectral power during auditory and audiovisual stimuli in the central regions. These findings suggest that multisensory oscillatory theta responses differ according to age, which further proves that the left frontal regions play an important role in audiovisual integration.