Simultaneous denoizing and segmentation of ink-jet printing images are challenging tasks for ink-jet printing pattern analysis. Thus, a unified denoizing-segmentation algorithm is proposed for ink-jet printing images. The energy functional for the proposed algorithm consists of three terms: the Beltrami flow, the phase field, and the shape prior. The Beltrami flow is used to enhance image features while preserving natural fine structures and the phase field model is introduced to extract specific pattern structures within ink-jet printing images. The shape prior term for the deformable framework through a nonlinear energy representation is designed to attract different shapes towards the Beltrami flow and the phase field at given directions. A fuzzy optimization method, called multi-start fuzzy optimization method (MSFOM), is also proposed to numerically solve the unified denoizing-segmentation model. MSFOM is a hybrid algorithm, combining fuzzy logic and genetic optimization, which is able to find the suboptimal global minimum with a low computational cost. Experimental results demonstrate that the proposed algorithm is better than the existing schemes, and it offers effective noise removal in noisy ink-jet printing images while maintaining fine structures of patterns.