DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-of-the-art approaches for a variety of natural images.Comment: ICCV 201

Heat transfer in MHD Ekman layer on a porous plate

A steady asymptotic solution for the temperature distribution in the case of flow past a porous plate in a rotating frame of reference is obtained. In particular, the temperature distribution for MHD Ekman layer on a porous flat plate is studied. It is seen that, while a steady asymptotic solution is possible in case of suction, no steady temperature field is possible in case of blowing. Further, from the results it is observed that suction and magnetic field have opposing influence on the rate of heat transfer. © 1978 Società Italiana di Fisica

To Evaluate and Compare the Efficacy of Vidarikanda Churna and Kataka Churna in the Management of Male Sexual Function and Visual Acuity

Background: Eye is an important sense organ. It is very important to protect vision. Aacharya Sushruta described Atimaithuna (excessive sexual activity), in etiological factors causing Netraroga. Excessive sexinduced stress hormones – epinephrine and nor-epinephrine – may damage retinal endothelial cells, inflame eye balls and dilate pupils, over sensitivity to light in the retina and adverse effect on power of vision.Need of study: Most ophthalmologists can effectively diagnose and treat blurred vision caused by glaucoma, cataracts, presbyopia, diabetes, macular degeneration or retinal detachment. But for sexually exhausted people with blurred vision, the problem goes undiagnosed and treated.Aim: To evaluate and compare the efficacy of Vidarikanda churna and Kataka churna in the management of male sexual function and poor vision.Materials and Methods: 110 patients who had Timira (refractive errors) with associated symptoms of male sexual dysfunction (MSD) were selected for randomized control trial on the basis of prepared inclusion and exclusion criteria; out of them 50 patients each were divided in two groups (excluding drop outs)named Group A and Group B.Results: The trial drug Kataka churna showed statistically significant results in subjective parameters of visual disturbances (Timira roga) and visual acuity. Vidarikanda churna significantly improved the quality of vision and MSD.Conclusion: The study overall concluded that Shukravradhaka drugs like Vidarikanda significantly improve the quality of vision

Whole-body Detection, Recognition and Identification at Altitude and Range

In this paper, we address the challenging task of whole-body biometric detection, recognition, and identification at distances of up to 500m and large pitch angles of up to 50 degree. We propose an end-to-end system evaluated on diverse datasets, including the challenging Biometric Recognition and Identification at Range (BRIAR) dataset. Our approach involves pre-training the detector on common image datasets and fine-tuning it on BRIAR's complex videos and images. After detection, we extract body images and employ a feature extractor for recognition. We conduct thorough evaluations under various conditions, such as different ranges and angles in indoor, outdoor, and aerial scenarios. Our method achieves an average F1 score of 98.29% at IoU = 0.7 and demonstrates strong performance in recognition accuracy and true acceptance rate at low false acceptance rates compared to existing models. On a test set of 100 subjects with 444 distractors, our model achieves a rank-20 recognition accuracy of 75.13% and a TAR@1%FAR of 54.09%

CapsFlow: Optical Flow Estimation with Capsule Networks

We present a framework to use recently introduced Capsule Networks for solving the problem of Optical Flow, one of the fundamental computer vision tasks. Most of the existing state of the art deep architectures either uses a correlation oepration to match features from them. While correlation layer is sensitive to the choice of hyperparameters and does not put a prior on the underlying structure of the object, spatio temporal features will be limited by the network's receptive field. Also, we as humans look at moving objects as whole, something which cannot be encoded by correlation or spatio temporal features. Capsules, on the other hand, are specialized to model seperate entities and their pose as a continuous matrix. Thus, we show that a simpler linear operation over poses of the objects detected by the capsules in enough to model flow. We show reslts on a small toy dataset where we outperform FlowNetC and PWC-Net models.Comment: Newer version added to correct issue in the conference name of the previous version uploaded on April 1s

Few-Shot Domain Adaptation for Low Light RAW Image Enhancement

Enhancing practical low light raw images is a difficult task due to severe noise and color distortions from short exposure time and limited illumination. Despite the success of existing Convolutional Neural Network (CNN) based methods, their performance is not adaptable to different camera domains. In addition, such methods also require large datasets with short-exposure and corresponding long-exposure ground truth raw images for each camera domain, which is tedious to compile. To address this issue, we present a novel few-shot domain adaptation method to utilize the existing source camera labeled data with few labeled samples from the target camera to improve the target domain's enhancement quality in extreme low-light imaging. Our experiments show that only ten or fewer labeled samples from the target camera domain are sufficient to achieve similar or better enhancement performance than training a model with a large labeled target camera dataset. To support research in this direction, we also present a new low-light raw image dataset captured with a Nikon camera, comprising short-exposure and their corresponding long-exposure ground truth images.Comment: BMVC 2021 Best Student Paper Award (Runner-Up). Project Page: https://val.cds.iisc.ac.in/HDR/BMVC21/index.htm

Synthesis and Bronchodilator Studies of Some Novel 6-Alkyl/Aryl-1,2,4-Triazino[4,3-c]Quinazolines

A series of alkyl- and aryl-1,2,4-triazino[4,3-c]quinazolines (5a-h and 8a-h) were synthesized and characterized. The title compounds were evaluated for their in vivo bronchodilator activity on guinea pigs. All the test compounds exhibited good protection against histamine-induced bronchospasm. The structure-activity relationships based on the results obtained for these series were studied. Incorporation of an aryl ring with halo substitution to the theophylline bioisostere increases its potency. Among the compounds tested, 5b was found to be the most potent with 88.7% protection against histamine-induced bronchospasm compared to the standard compound aminophylline (87.8%)

System of crop intensification for more productive, resource-conserving, climate-resilient, and sustainable agriculture: experience with diverse crops in varying agroecologies

With continually increasing demand for food accompanied by the constraints of climate change and the availability and quality of soil and water, the world’s farmers are challenged to produce more food per hectare with less water, and with fewer agrochemical inputs if possible. The ideas and methods of the system of rice intensification which is improving irrigated rice production are now being extended/adapted to many other crops: wheat, maize, finger millet, sugarcane, tef, mustard, legumes, vegetables, and even spices. Promoting better root growth and enhancing the soil’s fertility with organic materials are being found effective means for raising the yields of many crop plants with less water, less fertilizer, reduced seeds, fewer agrochemicals, and greater climate resilience. In this article, we review what is becoming known about various farmer-centred innovations for agroecological crop management that can contribute to agricultural sustainability. These changes represent the emerging system of crop intensification, which is being increasingly applied in Asian, African, and Latin American countries. More research will be needed to verify the efficacy and impact of these innovations and to clarify their conditions and limits. But as no negative effects for human or environmental health have been identified, making these agronomic options more widely known should prompt more investigation and, to the extent justified by results, utilization of these methodologies