UIEB dataset - feature extractor and classifier.pptx

UIEB dataset

1. UIEB dataset - feature extractor and classifier

2. Introduction The UIEB (Underwater Image Enhancement Benchmark) dataset is a specialized collection of underwater images designed to facilitate research in underwater image enhancement. It was created to address the challenges posed by underwater imaging, such as color distortion, low contrast, and blurring due to the scattering and absorption of light in water.

3. Purposes of the UIEB Dataset Benchmarking : The primary purpose of the UIEB dataset is to provide a standardized benchmark for evaluating underwater image enhancement algorithms. Researchers can use this dataset to compare the performance of different methods on a common set of images. Algorithm Development : The dataset serves as a valuable resource for developing and testing new underwater image enhancement techniques. By providing a diverse set of challenging images, it helps in designing robust algorithms that can handle various underwater conditions. Improving Visual Quality : The ultimate goal of using the UIEB dataset is to improve the visual quality of underwater images, making them more suitable for applications like marine biology, underwater archaeology, and recreational diving photography.

4. Composition of the UIEB Dataset The UIEB dataset consists of two main components: Raw Underwater Images : This component includes a large number of raw underwater images captured under different conditions. These images exhibit various types of degradations such as color casts (e.g., blue or green tint), low contrast, blurriness, and haze. Reference Images : For a subset of the raw images, the UIEB dataset provides reference images. These reference images are manually enhanced by professional photographers to serve as ground truth for evaluating the performance of enhancement algorithms. The reference images offer a visual goal for what the enhanced images should ideally look like.

5. Key Features Diversity : The dataset includes images taken in different underwater environments, with varying levels of visibility, depth, and lighting conditions. This diversity ensures that enhancement algorithms can be tested against a wide range of scenarios. Ground Truth : The inclusion of reference images allows for quantitative evaluation of enhancement algorithms using metrics like PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index). Availability : The UIEB dataset is publicly available, making it accessible to the research community. This openness promotes collaboration and benchmarking across different research groups.

6. Applications Marine Research : Enhanced underwater images can help marine biologists study marine life and ecosystems more effectively by providing clearer and more accurate visual data. Underwater Exploration : Archaeologists and explorers can use enhanced images to better document and analyze underwater sites and artifacts. Photography and Videography : Recreational and professional underwater photographers and videographers can improve the aesthetic quality of their images and videos. Autonomous Underwater Vehicles (AUVs): Improved visual data can enhance the performance of AUVs used for underwater mapping, inspection, and surveillance.

7. Challenges Addressed Correction : The dataset helps in developing algorithms that correct color casts caused by the absorption and scattering of light in water. Contrast Enhancement : It aids in improving the contrast of underwater images, making objects and details more distinguishable. Dehazing : The dataset supports the development of techniques to remove the haze effect caused by suspended particles in water.

8. Conclusion The UIEB dataset is a crucial resource for the underwater imaging community, providing a comprehensive benchmark for enhancing the quality of underwater images. Its diverse collection of raw and reference images enables researchers to develop and evaluate algorithms that address the unique challenges of underwater photography, ultimately leading to better visual quality and more accurate underwater exploration and analysis.

9. Thank you By- Abhishek G Suman D C