Underwater Image Enhancement Using Deep Learning: A Multi-Stage Processing Approach

Capturing images beneath the water surface is fundamentally different from photography in air. Water selectively absorbs different wavelengths of light, scatters photons through suspended particles, and strips images of natural colour, contrast, and sharpness before they ever reach a sensor. The result is a degraded visual record that makes downstream tasks — coral reef surveys, pipeline inspections, autonomous vehicle navigation, and archaeological documentation — significantly harder than they need to be. This paper presents a systematic, multi-stage processing pipeline designed to address these degradation effects without requiring specialised underwater hardware. Starting from a raw degraded image, the system performs histogram-guided colour compensation on the red and blue channels using the green channel as a stable reference, applies Gray World white balancing to neutralise residual colour cast, and then branches into parallel enhancement paths: unsharp-masking for edge and detail recovery, and HSV-domain histogram equalisation for global contrast improvement. The two enhanced streams are subsequently fused through both an averaging strategy and a Principal Component Analysis (PCA) weighted combination. Quality is assessed quantitatively using Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) against reference images. Experimental results demonstrate measurable and visually convincing improvements across a representative set of underwater scenes. The modular design of the pipeline ensures that individual stages can be independently upgraded, making the system readily extensible as algorithmic advances emerge.