Computer Science Digital Image Analysis Assignment

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.