High dynamic range (HDR) images provide more realistic experience in displaying real-world scenes than conventional low dynamic range (LDR) images by providing much more detailed luminance information; However, most imaging content is still available in low dynamic range. Inverse tonemapping is known as the problem of inferring an HDR image from a single-exposure LDR image in which the lost data caused by saturation of bright parts and quantization must be reconstructed.To address this problem, in this thesis, two fully-automatic architectures based on convolutional neural networks, are proposed. Both these architectures utilize a number of convolutional auto-encoders as...