Learning Deep Context Sensitive Decomposition for Low Light Image Enhancement.
Enhancing the quality of low light (LOL) images plays a very important role in many image processing and multimedia applications. In recent years, a variety of deep learning techniques have been developed to address this challenging task. A typical framework is to simultaneously estimate the illumination and reflectance, but they disregard the scene level contextual information encapsulated in feature spaces, causing many unfavorable outcomes, e.g. details loss, color unsaturation, and artifacts. To address these issues, we develop a new context sensitive decomposition network (CSDNet) architecture to exploit the scene level contextual dependencies on spatial scales. More concretely, we build a two stream estimation mechanism including reflectance and illumination estimation network. We design a novel context sensitive decomposition connection to bridge the two stream mechanism by incorporating the physical principle. The spatially varying illumination guidance is further constructed for achieving the edge aware smoothness property of the illumination component. According to different training patterns, we construct CSDNet (paired supervision) and context sensitive decomposition generative adversarial network (CSDGAN) (unpaired supervision) to fully evaluate our designed architecture. We test our method on seven testing benchmarks [including massachusetts institute of technology (MIT) Adobe FiveK, LOL, ExDark, and naturalness preserved enhancement (NPE) to conduct plenty of analytical and evaluated experiments. Thanks to our designed context sensitive decomposition connection, we successfully realized excellent enhanced results (with sufficient details, vivid colors, and few noises) which fully indicates our superiority against existing state of the art approaches. Finally, considering the practical needs for high efficiency, we develop a lightweight CSDNet (named LiteCSDNet) by reducing the number of channels. Furthermore, by sharing an encoder for these two components, we obtain a more lightweight version (SLiteCSDNet for short) SLiteCSDNet just contains 0.0301M parameters but achieves the almost same performance as CSDNet. Code is available at https:/github.com/KarelZhang/CSDNet CSDGAN.
IEEE transactions on neural networks and learning systems