Underwater image denoising using adaptive wavelet subband thresholding

Recently, image denoising using the wavelet transform has been attracting much attention. Wavelet based approach provides a particularly useful method for image denoising when the preservation of image features in the scene is of importance. In this paper, we propose a novel denoising method for removing additive noise present in the underwater images. In addition to scattering and absorption effects, macroscopic floating particles producing images of the size of a pixel can be present as well due to sand raised by the motion of a diver, or small plankton particles. These particles are part of the scene, but cause generally unwanted signal. We see them as an additive noise. The problems it causes in feature extraction. In the proposed denoising method, first we use homomorphic filtering for correcting non uniform illumination, then we apply anisotropic filtering for smoothing. After smoothing, we apply adaptive wavelet subband thresholding with Modified Bayes-Shrink function. We compared and evaluated the proposed denoising method based on the Peak Signal to Noise Ratio (PSNR). The experimental result shows that the proposed method yields superior result for underwater noisy images compared to other denoising techniques.