Underwater vehicle reveals enhanced coral reefs through advanced imaging.

Dive Deep: How Nonlinear Filtering Clears the Waters of Underwater Imaging

Mira Elwood in Tech & Innovation August 2025 • 3 min read.

"Unlock the secrets of clearer underwater images with advanced FPGA-based nonlinear filtering techniques, revolutionizing ocean exploration and automated vehicle navigation."

Underwater vehicles are the unsung heroes of ocean exploration, tasked with surveying the ocean floor, inspecting pipelines, and detecting targets. These vehicles often rely on optical sensors to capture images, but the results are frequently compromised by the unique challenges of the underwater environment. These challenges include non-uniform lighting, low contrast, and limited range, making it difficult to obtain clear and usable images.

To combat these issues, advanced image processing techniques are essential. Optical images captured by autonomous underwater vehicles need pre processing as they suffer from various adversary underwater conditions and require low power devices such as FPGA.

One promising approach is nonlinear filtering, particularly when implemented using Field-Programmable Gate Arrays (FPGAs). FPGAs offer a powerful platform for image processing due to their ability to perform complex computations with high parallelism, making them ideal for real-time applications. This article delves into how FPGA-based nonlinear filtering can significantly enhance underwater images, opening up new possibilities for ocean exploration and automated underwater navigation.

Why Underwater Images Need a Helping Hand: Understanding the Challenges

Underwater vehicle reveals enhanced coral reefs through advanced imaging.

Underwater images often suffer from several common problems that degrade their quality. These include:

These limitations make it difficult for underwater vehicles to perform their tasks effectively. To overcome these challenges, researchers and engineers have turned to image enhancement techniques, particularly nonlinear filtering.

Non-Uniform Lighting: Sunlight is quickly scattered and absorbed as it penetrates water, leading to uneven illumination and shadows.
Low Contrast: The scattering of light reduces the contrast between objects, making it difficult to distinguish details.
Limited Range: Water absorbs light, reducing the distance at which objects can be clearly seen.
Color Distortion: Water absorbs different colors at different rates, resulting in a loss of color fidelity. Red light is absorbed first, followed by yellow and green, leaving blue as the dominant color.

One proven method of enhancing underwater images is by filtering to correct non uniform illumination.

The Future is Clear: The Impact of Enhanced Underwater Imaging

The successful implementation of Fast Fourier Transform and Inverse Fast Fourier Transform using FPGA has led the way for further innovation. The FPGA implementation of image processing algorithms for the automation of underwater vehicles exploits various ocean related research work to be done with FPGA which offers higher speed and lesser power requirements. Compared to other DSPs used for hardware implementation, the cost and speed of FPGAs are appreciated for developing prototypes.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.17485/ijst/2015/v8i35/79110, Alternate LINK

Title: Fpga Implementation Of Underwater Image Enhancement Using Nonlinear Filtering

Subject: Multidisciplinary

Journal: Indian Journal of Science and Technology

Publisher: Indian Society for Education and Environment

Authors: S. M. Alex Raj, M. H. Supriya

Published: 2015-12-19

Everything You Need To Know

What specific challenges make underwater image clarity difficult, and how do these impact the effectiveness of underwater vehicles?

Underwater images face several obstacles that compromise their quality and the ability of autonomous underwater vehicles (AUVs) to function effectively. These challenges include non-uniform lighting caused by sunlight scattering and absorption, leading to uneven illumination and shadows. The scattering of light also results in low contrast, making it hard to distinguish details. Water's absorption of light limits the range at which objects can be seen clearly. Furthermore, color distortion occurs as water absorbs different colors at varying rates, with red light absorbed first, followed by yellow and green, leaving blue as the dominant color. These issues hinder AUVs in tasks like surveying the ocean floor, inspecting pipelines, and detecting targets, necessitating advanced image processing techniques to overcome these limitations.

How does nonlinear filtering, particularly when implemented using Field-Programmable Gate Arrays (FPGAs), improve underwater image quality?

Nonlinear filtering, especially when implemented with Field-Programmable Gate Arrays (FPGAs), significantly enhances underwater image quality. FPGAs are ideal for real-time image processing because they can perform complex computations with high parallelism. This allows for the correction of issues such as non-uniform lighting and low contrast, which are common in underwater images. By using FPGAs, the filtering process can be optimized for speed and efficiency, providing clearer visuals for ocean exploration and autonomous vehicle navigation despite the challenging underwater conditions.

Why are Field-Programmable Gate Arrays (FPGAs) preferred over other processing methods for underwater image processing?

Field-Programmable Gate Arrays (FPGAs) are preferred for underwater image processing due to their powerful capabilities. FPGAs offer a platform for image processing due to their ability to perform complex computations with high parallelism. This high degree of parallelism allows FPGAs to process images in real-time, which is crucial for applications like automated underwater vehicle navigation. Compared to other Digital Signal Processors (DSPs) used for hardware implementation, FPGAs offer a balance of speed and cost effectiveness, making them ideal for developing prototypes and deploying image enhancement algorithms.

What are the key applications and impacts of improved underwater image quality, enabled by FPGA-based nonlinear filtering?

The improvement in underwater image quality, achieved through FPGA-based nonlinear filtering, has significant impacts on several applications. Enhanced visuals enable more effective ocean exploration, allowing researchers to study the ocean floor in greater detail. It also benefits automated underwater vehicle navigation by improving the ability of AUVs to survey the ocean floor, inspect pipelines, and detect targets. This technology leads to advancements in ocean-related research by providing clearer and more reliable data for various underwater tasks.

Could you explain, in simple terms, the concept of nonlinear filtering and how it addresses the problems in underwater images?

Nonlinear filtering is a technique used to improve the quality of underwater images by correcting the various distortions and limitations that occur. Underwater images often suffer from issues like uneven lighting, low contrast, limited range, and color distortion. Nonlinear filtering, when applied using Field-Programmable Gate Arrays (FPGAs), works by processing each pixel's value based on its relationship to other pixels in the image, effectively adjusting for these problems. This helps to enhance the clarity and detail of the images, making it easier to see objects and features in the underwater environment.