High Dynamic Range Image Reconstruction using Deep Learning

HDR image reconstruction is a technique that aims to generate high-quality images with a wider range of brightness and colors than traditional imaging methods In recent years, deep learning and residual networks have been utilized in various image processing tasks, including HDR image reconstruction. This proposed method utilizes a deep residual network to predict an HDR image from a single LDR image with high accuracy. The method involves two stages: a training stage and a testing stage. During training, the network is trained using a dataset of LDR-HDR image pairs to learn to map the LDR images to their corresponding HDR images. In the testing stage, the trained network is used to reconstruct an HDR image from single LDR images. Results from experiments demonstrate that the proposed method outperforms existing HDR reconstruction methods in terms of objective metrics and visual quality. This method provides a promising solution for generating high-quality HDR images from multiple LDR images and can have applications in various fields, including photography, computer graphics, and medical imaging.