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Underwater imaging presents unique challenges, notably color distortions and reduced contrast due to light attenuation and scattering. Most underwater image enhancement methods first use linear transformations for color compensation and then enhance the image. We observed that linear transformation for color compensation is not suitable for certain images. For such images, non-linear mapping is a better choice. This paper introduces a unique underwater image restoration approach leveraging a streamlined convolutional neural network (CNN) for dynamic weight learning for linear and non-linear mapping. In the first phase, a classifier is applied that classifies the input images as Type I or Type II. In the second phase, we use the Deep Line Model (DLM) for Type-I images and the Deep Curve Model (DCM) for Type-II images. For mapping an input image to an output image, the DLM creatively combines color compensation and contrast adjustment in a single step and uses deep lines for transformation, whereas the DCM employs higher-order curves. Both models utilize lightweight neural networks that learn per-pixel dynamic weights based on the input image's characteristics. Comprehensive evaluations on benchmark datasets using metrics like peak signal-to-noise ratio (PSNR) and root mean square error (RMSE) affirm our method's effectiveness in accurately restoring underwater images, outperforming existing techniques. [ABSTRACT FROM AUTHOR]

Shape From Focus (SFF) reconstructs a scene's shape using a series of images with varied focus settings. However, the effectiveness of SFF largely depends on the Focus Measure (FM) used, which is prone to noise-induced inaccuracies in focus values. To address these issues, we introduce a perception-influenced factor to refine the traditional Focus Volume (FV) derived from a traditional FM. Owing to the strong relationship between the Difference of Gaussians (DoG) and how the visual system perceives edges in a scene, we apply it to local areas of the image sequence by segmenting the image sequence into non-overlapping blocks. This process yields a new metric, the Perceptual Focus Factor (PFF), which we combine with the traditional FV to obtain an enhanced FV and, ultimately, an enhanced depth map. Intensive experiments are conducted by using fourteen synthetic and six real-world data sets. The performance of the proposed method is evaluated using quantitative measures, such as Root Mean Square Error (RMSE) and correlation. For fourteen synthetic data sets, the average RMSE measure of 6.88 and correction measure of 0.65 are obtained, which are improved through PFF from an RMSE of 7.44 and correlation of 0.56, respectively. Experimental results and comparative analysis demonstrate that the proposed approach outperforms the traditional state-of-the-art FMs in extracting depth maps. [ABSTRACT FROM AUTHOR]

In the shape-from-focus (SFF) method, the quality of the 3D shape generated relies heavily on the focus measure operator (FM) used. Unfortunately, most FMs are sensitive to noise and provide inaccurate depth maps. Among recent FMs, the ring difference filter (RDF) has demonstrated excellent robustness against noise and reasonable performance in computing accurate depth maps. However, it also suffers from the response cancellation problem (RCP) encountered in multidimensional kernel-based FMs. To address this issue, we propose an effective and robust FM called the directional ring difference filter (DRDF). In DRDF, the focus quality is computed by aggregating responses of RDF from multiple kernels in different directions. We conducted experiments using synthetic and real image datasets and found that the proposed DRDF method outperforms traditional FMs in terms of noise handling and producing a higher quality 3D shape estimate of the object. [ABSTRACT FROM AUTHOR]

The economies all over the world that have been adversely affected by the COVID-19 pandemic have recently started to devise different strategies to mitigate its consequences. Therefore, in order to dwell deeper into the measures taken by the policy makers around the world, this paper specifically analyzes how the monetary policies have been devised, in response to COVID-19. For this purpose, this paper has taken into consideration a panel of 8 Asian economies that have been affected the most acutely by the virus, have faced multiple lockdowns, and have also experienced other economic restraints, due to this very phenomenon. In order to compare the possible monetary policy options, and their outcomes during the COVID-19 pandemic, this paper refers to the global recession shock, as a valid point of reference. In addition to this, in order to gain access to the empirical evidence, the ARDL methodology has been applied on the quarterly data from 2005Q3 to 2020Q3. The results of the study have indicated that various plans have been taken into consideration, so as to lessen the consequences of these shocks that have trickled down into the respective economies of these countries. That is to put forth that, in the incidence of global recession, the monetary authorities have resorted to a less prudent stance. Whereas, more flexibility, through a persistent decrease in the policy rate has been observed since the pandemic first hit the world. In this regard, our results imply that a successful, efficient and effective response to the economic consequences of COVID-19, would ideally entail a set of remedial policies and structural reforms. [ABSTRACT FROM AUTHOR]

Dengue is a mosquito‐borne viral illness that infects humans. For the past few decades, it has been declared a global public health problem. The current study, conducted at the district headquarter hospital (DHQ) Bannu between June to September 2018, was based on the seroprevalence of antibodies against dengue virus serotypes and their hematological parameters among the patients. A total of 1738 individuals suspected of having dengue were diagnosed through NS1, IgG, and IgM antibodies and RT‐PCR techniques. Of all the samples, 716 (41.19%) were found to be positive for dengue. A higher infection rate was found in males (65.92%) compared with females (34.07%). The most affected age group was 16–40 years, whereas the most affected tehsil was Bannu, where the DENV‐3 serotype was prevalent. The rare serotype (DENV‐4) was found in 1% of cases. Symptoms including fever (100%), myalgia (100%), headache (61.31%), vomiting (34.63%), and rashes were common among the dengue patients. However, the mild cases showed fewer clinical signs compared with the severely infected cases. The study also revealed a significant association (P < 0.05) between hematological parameters and dengue infection, showing a significant decrease in TC, eosinophils, neutrophils, and platelets and a significant increase in monocytes and lymphocytes. Based on the current report, it is concluded that patients with the above symptoms and hematological changes may have an increased probability of dengue and should be kept under observation to separate dengue‐positive patients and to enhance the treatment process. [ABSTRACT FROM AUTHOR]