The objective of this study is to examine the problem of monocular depth estimation, which is essential for understanding a particular scene. The application of deep neural networks in generative models has resulted in significant progress in the precision and efficiency of depth estimations derived from a solitary image. However, most previous approaches have shown shortcomings in accurately calculating the depth barrier, resulting in less than optimal outcomes. Image restoration refers to the procedure of enhancing the quality of an image that has been degraded or has reduced clarity. This study introduces a novel and direct method that utilises the attention channel of the depth-to-depth network. This network has encoded elements that are useful for guiding the process of creating depth. The attention channel network consists exclusively of convolution layers and the Squeeze-And-Excitation Network (SENet).