Can anyone create me any one from the assignment doc
1. Real-Time Image Super-Resolution for Video Streams
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Description: Develop a system to enhance the resolution of low-quality video streams in real-time using
Super-Resolution GANs (SRGAN) or
ESRGAN.
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Challenge: Ensure temporal consistency across frames, avoid artifacts, and maintain real-time performance.
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Applications: Surveillance systems, live streaming platforms, and video conferencing tools.
2. Automated Medical Image Enhancement Using GANs
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Description: Build a model to enhance low-quality medical images (e.g., X-rays, MRIs) by reducing noise and improving contrast and detail.
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Challenge: Preserve diagnostic features and ensure regulatory compliance for medical-grade accuracy.
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Applications: Early diagnosis of diseases, improved radiology analysis, and telemedicine.
3. Misinformation Detection via Image Tampering Analysis
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Description: Detect manipulated or tampered regions in images using
deep learning techniques like CNNs combined with forensic feature analysis.
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Challenge: Handling sophisticated manipulations (e.g., deepfakes, copy-move forgeries).
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Applications: Fake news detection, forensic investigations, and social media content moderation.
4. Autonomous Image-Based Traffic Sign Recognition System
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Description: Develop a real-time system to detect and classify traffic signs from dashcam footage using
convolutional neural networks (CNNs).
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Challenge: Handle varying weather, lighting conditions, and occluded or damaged signs.
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Applications: Autonomous vehicles, advanced driver assistance systems (ADAS).
5. Emotion Detection from Facial Images
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Description: Create a model to detect human emotions (e.g., happy, sad, angry) from facial expressions in images using
deep learning models like VGG or ResNet.
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Challenge: Deal with subtle facial expression variations and diverse datasets.
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Applications: Mental health monitoring, human-computer interaction, and customer experience analysis.
6. Underwater Image Enhancement System
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Description: Improve visibility and color balance in underwater images using
image restoration techniques like dehazing, deblurring, and color correction.
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Challenge: Account for light scattering, absorption, and varied water conditions.
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Applications: Marine biology research, underwater photography, and ocean exploration.
7. Automated Aerial Image Object Detection
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Description: Design a system to detect objects like buildings, vehicles, or trees in aerial or satellite images using
YOLO (You Only Look Once) or
Faster R-CNN.
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Challenge: Handle high-resolution images, occlusions, and overlapping objects.
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Applications: Urban planning, disaster response, and environmental monitoring.
8. Smart Image-Based Dress Code Detection System
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Description: Build a system that identifies whether a person’s attire complies with a predefined dress code using
CNNs and
Transfer Learning.
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Challenge: Account for cultural variations, lighting conditions, and diverse clothing styles.
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Applications: Corporate environments, educational institutions, and security checkpoints.
9. Advanced Image Style Transfer System
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Description: Implement a system to transfer the artistic style of one image (e.g., Van Gogh’s painting) to another using
Neural Style Transfer (NST).
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Challenge: Preserve content while applying complex artistic styles efficiently for high-resolution images.
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Applications: Content creation, digital art, and advertising.
10. AI-Powered Wildlife Monitoring System
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Description: Develop a model to identify and track animals in wildlife images or videos using
deep learning and motion detection algorithms.
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Challenge: Handle low-light conditions, camouflage, and dynamic environments.
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Applications: Wildlife conservation, poaching prevention, and ecosystem monitoring.
