Underwater images received by underwater robots in unrestricted environment during underwater operations are characterized by overall bluish and greenish tones, blurrier edge details, and low contrast. This phenomenon is due to the attenuation and scattering of light in the water and the influence of artificial light sources. To improve the visual performance of underwater imaging, we propose a two-step method of improved gated context aggregation network and gray world algorithms. First, based on similarities between the underwater optical imaging model and the atmosphere model, a gray world algorithm is used to calibrate the image. Then, the corrected underwater images are inputted into an improved gated contextual aggregation network, which is utilized to fuse the features at different levels within the images. The introduction of convolutional block attention module and residual structure can effectively improve the feature extraction ability and prevent network degradation. The method eliminates the grid artifact phenomenon and improves the flexibility of channel information to achieve image enhancement. By conducting experiments, we then compare the performance of the proposed method with six classical state-of-the-art methods. The qualitative results confirm that the proposed method is capable of effectively removing haze, correcting the color deviation of underwater images, and maintaining the naturalness of images. We further perform a quantitative evaluation and show that the proposed method outperforms the other methods that are compared with the proposed in terms of peak signal-to-noise ratio and structural similarity . The enhancement results are also measured by employing the information entropy and underwater color image quality evaluation index, indicating that the proposed method exhibits the highest mean values of 8.0579, 8.1194, and 0.6182, 0.5914 on the two datasets. The experimental results collectively validate the effectiveness of the proposed method in improved underwater image blur.