Large visual language model (LVLM) demonstrate remarkable capabilities in understanding visual information and generating verbal expressions. However, LVLM are often affected by the phenomenon of object hallucinations, where the outputs appear plausible but do not align with the visual information in the images. This discrepancy between the generated text and the images presents a significant challenge in achieving accurate image-to-text alignment. To address this issue, this study identifies the lack of object attention as a key factor contributing to object hallucinations. To mitigate this, the proposed image contrast enhancement (ICE) method is introduced. ICE is a simple, user-friendly approach that compares the output distributions from both the original and the augmented visual inputs. This method enhances the model’s ability to perceive images more accurately, ensuring that the generated content aligns closely with the visual input and produces contextually consistent outputs. Experimental results demonstrate that the ICE method effectively mitigates object hallucinations across various LVLM without requiring additional training or external tools. Furthermore, the method performs well on the MME benchmark test for large-scale visual language models, indicating its broad applicability and effectiveness. The code will be released at ChangGuiyong/ICE.