Learning Hadamard-Product-Propagation for Image Dehazing and Beyond

Image dehazing has evolved into an attractive research field in the computer vision community in the past few decades. Previous traditional approaches attempt to design energy-based objective functions. However, they cannot accurately express the intrinsic characteristics of the images, posing weak adaptation ability for real-world complex scenarios. More recently, deep learning techniques for image dehazing have matured and become more reliable, showing outstanding performance. Nevertheless, these methods heavily depend on training data, restricting their application ranges. More importantly, both traditional and deep learning approaches all ignore a common issue, noises/artifacts always appear in the recovery process. To this end, a new Hadamard-Product (HP) model is proposed, which consists of a series of data-driven priors. Based on this model, we derive a Learnable Hadamard-Product-Propagation (LHPP) by cascading a series of principle-inspired guidance and recovery modules. In which, the principle-inspired guidance related to transmission is endowed the smoothness property, the other recovery module satisfies the distribution of natural images. The Hadamard-product-based propagations is generated in our developed learnable framework for the task of image dehazing. In this way, we can eliminate noises/artifacts in the recovery procedure to obtain the ideal outputs. Subsequently, since the generality of our HP model, we successfully extend our LHPP to settle low-light image enhancement and underwater image enhancement problems. A series of analytical experiments are performed to verify our effectiveness. Plenty of performance evaluations on three complex tasks fully reveal our superiority against multiple state-of-the-art methods.