Reconstructing incomplete and randomly fragmented multicolor images is a core challenge in computer vision with broad relevance to digital restoration, medical imaging, and reliable data transmission. This study investigates the reconstruction of images that exhibit both missing regions and randomly distributed patterns, conditions under which traditional interpolation or diffusion-based methods often fail. We propose a hybrid framework that integrates probabilistic modeling with deep generative learning to infer missing content while preserving both structural coherence and color fidelity. The aim is to enhance spatial correlations, uncertainty modeling, and context-aware generative priors; the method effectively restores globally consistent images even under significant incompleteness. Experimental results demonstrate the outstanding performance of standalone probabilistic or deep learning approaches, achieving higher accuracy and improved perceptual quality across diverse levels of noise, color complexity, and missing data. The findings underscore the promise of hybrid reconstruction strategies in enhancing robustness and visual quality in practical image restoration scenarios.