We propose an underwater image enhancement model inspired by the morphology and function of the teleost fish retina. We aim to solve the problems of underwater image degradation raised by the ...blurring and nonuniform color biasing. In particular, the feedback from color-sensitive horizontal cells to cones and a red channel compensation are used to correct the nonuniform color bias. The center-surround opponent mechanism of the bipolar cells and the feedback from amacrine cells to interplexiform cells then to horizontal cells serve to enhance the edges and contrasts of the output image. The ganglion cells with color-opponent mechanism are used for color enhancement and color correction. Finally, we adopt a luminance-based fusion strategy to reconstruct the enhanced image from the outputs of ON and OFF pathways of fish retina. Our model utilizes the global statistics (i.e., image contrast) to automatically guide the design of each low-level filter, which realizes the self-adaption of the main parameters. Extensive qualitative and quantitative evaluations on various underwater scenes validate the competitive performance of our technique. Our model also significantly improves the accuracy of transmission map estimation and local feature point matching using the underwater image. Our method is a single image approach that does not require the specialized prior about the underwater condition or scene structure.
Image enhancement is an important pre-processing step for many computer vision applications especially regarding the scenes in poor visibility conditions. In this work, we develop a unified ...two-pathway model inspired by the biological vision, especially the early visual mechanisms, which contributes to image enhancement tasks including low dynamic range (LDR) image enhancement and high dynamic range (HDR) image tone mapping. Firstly, the input image is separated and sent into two visual pathways: structure-pathway and detail-pathway, corresponding to the M- and P-pathway in the early visual system, which code the low- and high-frequency visual information, respectively. In the structure-pathway, an extended biological normalization model is used to integrate the global and local luminance adaptation, which can handle the visual scenes with varying illuminations. On the other hand, the detail enhancement and local noise suppression are achieved in the detail-pathway based on local energy weighting. Finally, the outputs of structure-and detail-pathway are integrated to achieve the low-light image enhancement. In addition, the proposed model can also be used for tone mapping of HDR images with some fine-tuning steps. Extensive experiments on three datasets (two LDR image datasets and one HDR scene dataset) show that the proposed model can handle the visual enhancement tasks mentioned above efficiently and outperform the related state-of-the-art methods.
Cervical cancer is common cancer among women with high morbidity. MicroRNAs (miRs) are involved in the progression and development of cervical cancer. This study aimed to explore the effect of ...miR‐99b‐5p (miR‐99b) on invasion and migration in cervical cancer through the phosphatidylinositol 3‐kinase (PI3K)/protein kinase B (AKT)/mechanistic target of rapamycin (mTOR) signaling pathway. The microarray‐based analysis was used to screen out differentially expressed miRNAs. Expression of miR‐99b, PI3K, AKT, mTOR, and ribosomal protein S6 kinase (p70S6K) was determined in both cervical cancer tissues and paracancerous tissues. Next, alteration of miR‐99b expression in cervical cancer was conducted to evaluate levels of PI3K, AKT, mTOR, p70S6K matrix metallopeptidase 2, epithelial cell adhesion molecule, and intercellular adhesion molecule 1, as well as the effect of miR‐99b on cell proliferation, invasion, migration, cell cycle distribution, and apoptosis. The results demonstrated that miR‐99b expression was decreased and levels of PI3K, AKT, mTOR, and p70S6K were elevated in cervical cancer tissues. More important, overexpressed miR‐99b repressed the PI3K/AKT/mTOR signaling pathway, inhibited cell proliferation, invasion, and migration, blocked cell cycle entry, and promoted apoptosis in cervical cancer. These results indicate that miR‐99b attenuates the migration and invasion of human cervical cancer cells through downregulation of the PI3K/AKT/mTOR signaling pathway, which provides a therapeutic approach for cervical cancer treatment.
These results indicate that miR‐99b attenuates the migration and invasion of human cervical cancer cells through downregulation of the phosphatidylinositol 3‐kinase/protein kinase B/mechanistic target of rapamycin signaling pathway, which provides a therapeutic approach for cervical cancer treatment.
Zinc finger protein 667‐antisense RNA 1 (ZNF667‐AS1), located on human chromosome 19q13.43, is a member of the C2H2 zinc finger protein family. Herein, we aimed to analyze the interactions between ...ZNF667‐AS1, microRNA‐93‐3p (miR‐93‐3p), and paternally expressed gene 3 (PEG3) and to explore their roles in the tumorigenesis of cervical cancer (CC). Differentially expressed long noncoding RNAs and miRNAs related to CC were determined using gene expression datasets sourced from the Gene Expression Omnibus database. Subsequently, the regulatory relationships between ZNF667‐AS1 and miR‐93‐3p and between miR‐93‐3p and PEG3 were identified using the dual‐luciferase reporter gene assay. In addition, the expression of miR‐93‐3p and ZNF667‐AS1 was up‐ or downregulated in CC cells (HeLa), in order to assess their effects on cell cycle distribution and cell invasion in vitro, and tumor growth and metastasis in vivo. MiR‐93‐3p was found to be highly expressed, while ZNF667‐AS1 and PEG3 were poorly expressed in CC. ZNF667‐AS1 could competitively bind to miR‐93‐3p, which targeted PEG3. In addition, miR‐93‐3p downregulation and ZNF667‐AS1 overexpression led to increased expression of PEG3, tissue inhibitor of metalloproteinases, and p16 and decreased expression of cyclin D1, matrix metalloproteinase‐2 and ‐9. MiR‐93‐3p inhibition and ZNF667‐AS1 elevation also inhibited cell cycle entry and cell invasion in vitro, but repressed tumor growth and metastasis in vivo. These key findings demonstrated that upregulation of ZNF667‐AS1 could suppress the progression of CC via the modulation of miR‐93‐3p‐dependent PEG3, suggesting a potential therapeutic target for the treatment of CC.
Schematic diagram of the regulation of Zinc finger protein 667‐antisense RNA 1 (ZNF667‐AS1) in cervical cancer. ZNF667‐AS1 negatively regulated microRNA‐93‐3p expression by targeting paternally expressed gene 3 (PEG3), thereby restraining cell proliferation and EMT. PEG3 increased the expression of tissue inhibitor of metalloproteinases and p16 and further decreased the expression of matrix metalloproteinase‐2 and ‐9.
Multi-illuminant-based color constancy (MCC) is quite a challenging task. In this paper, we proposed a novel model motivated by the bottom-up and top-down mechanisms of human visual system (HVS) to ...estimate the spatially varying illumination in a scene. The motivation for bottom-up based estimation is from our finding that the bright and dark parts in a scene play different roles in encoding illuminants. However, handling the color shift of large colorful objects is difficult using pure bottom-up processing. Thus, we further introduce a top-down constraint inspired by the findings in visual psychophysics, in which high-level information (e.g., the prior of light source colors) plays a key role in visual color constancy. In order to implement the top-down hypothesis, we simply learn a color mapping between the illuminant distribution estimated by bottom-up processing and the ground truth maps provided by the dataset. We evaluated our model on four datasets and the results show that our method obtains very competitive performance compared with the state-of-the-art MCC algorithms. Moreover, the robustness of our model is more tangible considering that our results were obtained using the same parameters for all the datasets or the parameters of our model were learned from the inputs, that is, mimicking how HVS operates. We also show the color correction results on some real-world images taken from the web.
We define the task of salient structure (SS) detection to unify the saliency-related tasks, such as fixation prediction, salient object detection, and detection of other structures of interest in ...cluttered environments. To solve such SS detection tasks, a unified framework inspired by the two-pathway-based search strategy of biological vision is proposed in this paper. First, a contour-based spatial prior (CBSP) is extracted based on the layout of edges in the given scene along a fast non-selective pathway, which provides a rough, task-irrelevant, and robust estimation of the locations where the potential SSs are present. Second, another flow of local feature extraction is executed in parallel along the selective pathway. Finally, Bayesian inference is used to auto-weight and integrate the local cues guided by CBSP and to predict the exact locations of SSs. This model is invariant to the size and features of objects. The experimental results on six large datasets (three fixation prediction datasets and three salient object datasets) demonstrate that our system achieves competitive performance for SS detection (i.e., both the tasks of fixation prediction and salient object detection) compared with the state-of-the-art methods. In addition, our system also performs well for salient object construction from saliency maps and can be easily extended for salient edge detection.
Saliency detection, an important step in many computer vision applications, can, for example, predict where drivers look in a vehicular traffic environment. While many bottom-up and top-down saliency ...detection models have been proposed for fixation prediction in outdoor scenes, no specific attempt has been made for traffic images. Here, we propose a learning saliency detection model based on a random forest (RF) to predict drivers' fixation positions in a driving environment. First, we extract low-level (color, intensity, orientation, etc.) and high-level (e.g., the vanishing point and center bias) features and then predict the fixation points via RF-based learning. Finally, we evaluate the performance of our saliency prediction model qualitatively and quantitatively. We use quantitative evaluation metrics that include the revised receiver operating characteristic (ROC), the area under the ROC curve value, and the normalized scan-path saliency score. The experimental results on real traffic images indicate that our model can more accurately predict a driver's fixation area, while driving than the state-of-the-art bottom-up saliency models.
Color Constancy Using Double-Opponency Gao, Shao-Bing; Yang, Kai-Fu; Li, Chao-Yi ...
IEEE transactions on pattern analysis and machine intelligence,
2015-Oct.-1, 2015-Oct, 2015-10-1, 20151001, Letnik:
37, Številka:
10
Journal Article
Recenzirano
The double-opponent (DO) color-sensitive cells in the primary visual cortex (V1) of the human visual system (HVS) have long been recognized as the physiological basis of color constancy. In this work ...we propose a new color constancy model by imitating the functional properties of the HVS from the single-opponent (SO) cells in the retina to the DO cells in V1 and the possible neurons in the higher visual cortexes. The idea behind the proposed double-opponency based color constancy (DOCC) model originates from the substantial observation that the color distribution of the responses of DO cells to the color-biased images coincides well with the vector denoting the light source color. Then the illuminant color is easily estimated by pooling the responses of DO cells in separate channels in LMS space with the pooling mechanism of sum or max. Extensive evaluations on three commonly used datasets, including the test with the dataset dependent optimal parameters, as well as the intraand inter-dataset cross validation, show that our physiologically inspired DOCC model can produce quite competitive results in comparison to the state-of-the-art approaches, but with a relative simple implementation and without requiring fine-tuning of the method for each different dataset.
In this paper, we relate the operation of image dynamic range adjustment to the following two tasks: 1) for a high dynamic range (HDR) image, its dynamic range will be mapped to the available dynamic ...range of display devices and 2) for a low dynamic range (LDR) image, its distribution of intensity will be extended to adequately utilize the full dynamic range of display devices. The common goal of both tasks is to preserve or even enhance the details and improve the visibility of scenes when being matched to the available dynamic range of a display device. In this paper, we propose an efficient method for image dynamic range adjustment with three adaptive steps. First, according to the histogram of the luminance map separated from the given RGB image, two suitable Gamma functions are adaptively selected to separately adjust the luminance of the dark and bright components. Second, an adaptive fusion strategy is proposed to combine the two adjusted luminance maps in order to balance the enhancement of the details in different regions. Third, an adaptive luminance-dependent color restoration method is designed to combine the fused luminance map with the original color components to obtain more consistent color saturation between the images before and after dynamic range adjustment. Extensive experiments show that the proposed method can efficiently compress the dynamic range of HDR scenes with good contrast, clear details, and high structural fidelity of the original image appearance. In addition, the proposed method can also obtain promising performance when being used to enhance LDR nighttime images and greatly facilitates the object (car) detection in nighttime traffic scenes.
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