Underwater image processing method for fish localization and detection in submarine environment

• A new underwater image preprocessing method for underwater detection is proposed.
• This method consists of three procedures. One is image denoising, another is image segmentation via mean-shift algorithm, and the other is log likelihood ratio test.
• Poisson–Gauss mixture algorithm is proposed for noise reduction.
• Log-Likelihood ratio test is applied for robust fish detection.
• Experimental results outperform the state of the art methods.

Object detection is an important process in image processing, it aims to detect instances of semantic objects of a certain class in digital images and videos. Object detection has applications in many areas of computer vision such as underwater fish detection. In this paper we present a method for preprocessing and fish localization in underwater images. We are based on a Poisson–Gauss theory, because it can accurately describe the noise present in a large variety of imaging systems. In the preprocessing step we denoise and restore the raw images. These images are split into regions utilizing the mean shift algorithm. For each region, statistical estimation is done independently in order to combine regions into objects. The method is tested under different underwater conditions. Experimental results show that the proposed approach outperforms state of the art methods.