Depth from blur

The most accessible and appropriate approach to recording and storing the depth measurements collected from a scene is through a depth map; accurate depth maps are also essential in extended reality and movie production. This topic of study is both intriguing and beneficial, and lucrative. Several firms are developing depth estimates for a range of reasons. Some may use it to add effects (such as bokeh) to photos and selfies (portrait mode) based on distance from the camera; others, both aerial and terrestrial, may utilize it for replacing or supplementing existing sensors in autonomous vehicles. A depth map is a two-dimensional array with the x and y distance information corresponding to the array's rows and columns, as in a conventional picture. This study examines the single-image depth inference problem using focus and blur images. A comparative work that examines Carvalho's, Lee's and Laina's methods that produce a depth map from a single image is carried out in this thesis. Carvalho's method takes a single synthetic to defocus image as input, and the output is the depth map using D3-Net. Both Lee's and Laina's approaches build a depth map from a single image using encoder-decoder architecture and Residual Network (ResNet50), respectively. After that, the predicted depth maps were segmented into three classes (near, far, far away). This study aims to determine the best performing method for estimating the depth map using the New York University v2 dataset. Furthermore, we can use the segmentation results to navigate the cameras (drones, robots, autonomous vehicles, etc.). As the results show, Carvalho's method was the best in-depth map estimation because of the synthetic defocus image dataset. Nevertheless, Laina's method is the best segmentation in near and far areas. The experimental results demonstrate that the NYU v2 dataset used with these models achieved accuracy values of 99.8%, 99.0%, and 98.8% for Carvalho, Laina, and Lee, respectively, for the predicted depth map. For segmentation, the accuracy values were 77%, 55%, and 90% for Carvalho, Lee, and Laina, respectively. Keywords: Depth map, Blur, Recurrent Neural Network, Convolutional Neural Network, Deep Learning