On Wavelet-Based Methods for Noise Reduction of cDNA Microarray Images

Denoising is recognized as one of the mandatory preprocessing tasks in microarray image analysis. Sparse representations of image pixels are commonly exploited to develop efficient image denoising algorithms. Existing approaches to transform image pixels into sparse representations require computationally demanding optimization techniques or a huge amount of prior knowledge to learn the kernels. Nevertheless, due to the mathematical elegancy, different types of multiresolution analysis, in particular, the variants of wavelet transforms such as the discrete wavelet transform, stationary wavelet transform, and complex wavelet transform have been employed successfully to develop many high-performance microarray array image denoising algorithms. This article presents a review of the sequential development of the wavelet-based methods for microarray image denoising. The useful and well-known properties of wavelet coefficients have led to the development of these algorithms by exploiting the statistical nature of the coefficients of the image and noise. The objective of this article is to summarize the key features of these algorithms and provide constructive analysis through categorization and comparison. The surveyed methods are discussed with respect to algorithmic issues such as the type of wavelet transforms used, statistical models employed, computational complexity, and denoising performance metrics.