A Fast Image Dehazing Using Encoder–Decoder Deep Neural Network

The image quality is degraded in bad weather situations such as haze or fog. This problem can affect image processing applications such as computer vision, security, and some other real-time image processing systems. Hence, image dehazing is essential for these applications to improve their performance. There are many dehazing algorithms that are implemented earlier using the atmospheric light-scattering model and enhancement-based techniques. However, these algorithms are complex and consume more execution time to perform dehazing, which isn’t fit for real-time image processing applications. To overcome this drawback, an encoder–decoder deep neural network (EDDNN) is designed in this manuscript for fast image dehazing purposes. The proposed EDDNN contains a total of four layers, they are input, encoder, decoder, and output layers. The proposed EDDNN is trained and tested with the most popular dataset called Realistic single image dehazing (RESIDE). The proposed EDDNN is fast in execution that suits real-time image processing systems (RTIPS) and also effectively eliminates the haze effect from the image.