This repository showcases a collection of image processing projects and experiments, combining classical techniques with modern deep learning approaches. Each notebook explores a specific concept, ranging from image classification and filtering to color space transformations and generative models.
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Stages in Image Processing
It introduces the fundamental stages of image processing, including preprocessing, enhancement, and feature extraction, providing a solid foundation for all subsequent projects. -
HSB & RGB Color Spaces
It demonstrates conversions between HSB and RGB color spaces and manipulation techniques, helping to understand color representation in digital images. -
Image Filtering
It applies classical image filters such as blurring, sharpening, and edge detection, showcasing essential techniques for image enhancement and preprocessing. -
JPEG Compression
It explains the JPEG compression process and its impact on image quality and file size, offering insights into practical image storage and transmission. -
Automated Waste Classification Using VGG16
It implements a Transfer Learning approach with the VGG16 model to classify waste images into categories, highlighting real-world applications of deep learning in environmental monitoring. -
Brain Tumor Classification using ViT
It uses Vision Transformers (ViT) to detect and classify brain tumors from MRI scans, demonstrating advanced methods in medical image analysis. -
Face Generation using GAN
It employs Generative Adversarial Networks (GANs) to generate realistic human faces, illustrating the capabilities of generative models for creative and research purposes.