Underwater Image Enhancement Method Based on Feature Fusion.pptx

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1. Underwater Image Enhancement Method Based on Feature Fusion Neural Network Submitted by: SHYAMAPRASAD M S LMC22MCA2033

2. INTRODUCTION Aiming at the problems of uneven illumination of underwater image caused by supplementary illumination in deep-sea and night waters, image noise, low contrast and color deviation caused by sus -pended particles in water, a new underwater image enhancement method under non-uniform illumination is proposed . The heterogeneous feature fusion module is designed to fuse different levels and different levels of features, so as to improve the overall perception ability of the network to detail information and semantic information . Secondly , a new feature attention mechanism is designed to improve the traditional channel attention mechanism, and the improved channel attention and pixel attention mechanism are added to the heterogeneous feature fusion process to strengthen the ability of the network to extract pixel features with different turbidity.

3. EXISTING SYSTEM The existing systems for underwater image enhancement employ various techniques, such as color correction, contrast adjustment, and dehazing algorithms, to improve visibility and image quality. Some methods use adaptive filters to address the challenges posed by water absorption and scattering. Others focus on exploiting inherent features in underwater scenes to enhance details. It's a dynamic field with ongoing research to refine existing methods and develop new ones for effective underwater image enhancement.

4. PROPOSED SYSTEM Feature fusion neural networks aim to improve underwater image enhancement by effectively combining information from different image features. These networks often use multiple convolutional layers to extract various features, such as color, texture, and edges, from the input image. The key advantages of feature fusion networks include: 1. Comprehensive Information Integration: Feature fusion networks can capture and integrate diverse information from different image features, allowing for a more comprehensive understanding of the underwater scene. 2. Adaptability: These networks can adaptively learn to combine features, enabling them to handle varying underwater conditions and enhance images in a more context-aware manner. 3. Improved Generalization: Feature fusion enhances the network's ability to generalize across different underwater environments, making it more robust to diverse conditions compared to traditional methods. 4. End-to-End Learning: Some feature fusion networks are designed to learn end-to-end, optimizing the entire enhancement process without relying on handcrafted features or preprocessing steps. However, it's essential to note that the effectiveness of feature fusion networks depends on various factors, including the architecture design, dataset used for training, and specific underwater conditions. Research in this area continues to evolve, with ongoing efforts to refine and optimize feature fusion networks for better underwater image enhancement.

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