Efficient Image Enhancement Using Hybrid Wavelet Technique

Image enhancement models have been in vogue since digitization but stable techniques for image enhancements have not emerged as inherent problems persist and one of the major problems include multiplicative noise also referred to as speckle noise and blurred models which are central to the study of coherent imaging systems, such as synthetic aperture radar and sonar, and ultrasound and laser imaging. These models introduce two additional layers of difficulties with respect to the standard noise scenario, one is the noise is multiplied by the original image and the other is that the noise is not Gaussian. These features of multiplicative noise models preclude the direct application of most state-of-the-art algorithms, which are designed for solving unconstrained optimization problems where the objective has two terms - a quadratic data term by reflecting the additive and nature of the noise, plus a non-smooth regularize, which may be a total variation or wavelet-based Regularizer /prior). The model proposes to address these difficulties by converting the multiplicative model into a hybrid one to obtain an enhanced noise removed image and optimize the image.