In this thesis, phase information is utilized to address several issues in image processing applications; namely image quality assessment, image contrast enhancement, and visual object tracking. The classical two-dimensional (2D) melcepstrum features, which ignore the phase information by design, are enhanced with image phase to form the 2D complex mel-cepstrum features. While integrating the phase information with the existing ceptral features, the unwrapping of phase information is carried out. The 2D complex mel-cepstrum features are fed into a regression scheme to map the feature matrices to subjective scores for the assessment of image quality. A Fourier domain approach for contrast enhancement of microscopy images is developed. The enhancement framework determines the frequency components in which the phase transitions are signifi- cant. The significant spectrum components are amplified by a factor depending on the level of transitions. In this way, phase variations are translated into amplitude changes which directly contribute to the enhancement process. Selective variation, which is an extension to the classical total variation framework, is introduced to determine the appropriate parameter set for the enhancement framework. The selective variation scheme evaluates the variations of the image in the high-frequency regions. A visual object tracking scheme based on image phase information is proposed. The main aim of the proposed scheme is to reduce the computational complexity of cross-correlation based matching frameworks. Starting from the derivation of normalized cross-correlation function, the tracking solution is simplified to a phase minimization problem under certain assumptions. The utilization of look-up tables for phase shifts enables a further decrease in computational cost.