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One of the challenges in image processing involves single image deraining (SID). Existing methods do not exploit images at multi-scales and often overlook spatial and channel information. These methods fail to account for different rain conditions, leading to challenges in effectively removing a wide range of rain streak patterns, such as diverse directional or dense streaks. Moreover, they often result in the loss of fine texture details or a blurred background in the process of eliminating rain streaks from images captured in heavy rain. Mostly state-of-the-art (SOTA) deraining models achieve higher performance in removing rain from rainy images but at the expense of a high number of parameters, which results in computational complexity and memory requirements. Furthermore, they also do not consider high-level visioned evaluation metrics to perform deep evaluations of the proposed models. In this paper, a simple lightweight network with few parameters and relatively shallow depth is proposed by fusing the traditional Gaussian-Laplacian pyramid technique with the attention module. We propose a novel attention-based lightweight pyramid network for image deraining and detection (LPN-IDD) to achieve better deraining performance. The proposed model includes a dual attention module integrated with the Gaussian-Laplacian pyramids network. In LPN-IDD, different levels of Laplacian pyramids can extract multi-scale features to adapt to different shapes and types of rain streaks. Residual and recursive blocks are used in each subnetwork along with dual attention blocks to resist the occlusion or texture feature while suppressing unnecessary features. Extensive experimentation performed on the SID synthetic and real-world datasets demonstrate the effectiveness of the proposed model in image deraining tasks. For deep evaluation of the proposed methodology, object detection models including faster-RCNN, YOLO-V3, YOLO-V7 are used along with full reference evaluation metrics.

Objective: To determine the prevalence of urinary abnormalities in asymptomatic school children., Methods: This observational study was conducted at King Edward Medical University/Mayo Hospital Lahore. Children of 5-15 year of age from Central Model School, Lower Mall, Lahore were included from March to May, 2022. Children having prior renal or systemic disease, on steroids and menstruating girls at the time of urinalysis were excluded. In first screening, children's urine was collected in clean 20mL vessel and examined using urinary dipstick. Children with aberrant results were re-screened after two weeks., Result: Out of total 1600 children, majority were male 1416(88.5%) with mean age 11.92±2.47 years. Urinary abnormalities were observed in 278(17.38%) children on first screening and 86(5.38%) on second screening. Isolated proteinuria was present in 131(8.19%) children on first screening and 26(1.62%) on second screening. Isolated hematuria was present in 100(6.50%) on first screening and 33(2.06%) on second screening. Isolated pyuria was present in 24(1.50%) on first screening and 14(0.88%) on second screening. Combined proteinuria and hematuria were present in 10(0.62%) on first screening and two (0.12%) on second screening. Combined proteinuria and pyuria were present in three (0.19%) on first screening and three (0.19%) on second screening. Combined hematuria and pyuria were present in nine (0.56%) on first screening and five (0.31%) on second screening. Combined proteinuria, hematuria and pyuria was present in one (0.06%) on first screening and three (0.19%) on second screening., Conclusion: Urine screening is necessary for early detection and management of kidney diseases. Marked number of children was found having urinary abnormalities. Asymptomatic proteinuria was the most common finding reflecting underlying kidney pathology going undiagnosed., Competing Interests: Conflict of Interest: None., (Copyright: © Pakistan Journal of Medical Sciences.)