Edge-Aware Interactive Contrast Enhancement

Abstract

Contrast enhancement is required in many applications. Many studies have been conducted to perform contrast enhancement automatically, but most of them do not consider various personal preferences for contrast. We propose an edge-aware interactive contrast enhancement algorithm to enable a user to adjust image contrast easily according to his or her preference. A user provides a parameter for controlling the global brightness and two types of scribbles to darken or brighten local regions in an image. Then, the proposed algorithm generates an edge-aware mask by propagating the scribbles to nearby regions and restores an enhanced image through a neural network, called e-IceNet. The user can provide annotations iteratively until he or she obtains a desired image. We train e-IceNet on guidance images to yield reliable results for diverse input images. We also propose two differentiable losses to train e-IceNet effectively and reliably. Extensive experiments demonstrate that the proposed e-IceNet is capable of allowing users to enhance images satisfactorily with simple scribbles, as well as producing enhanced images automatically.

Keywords

• Interactive contrast enhancement
• personalized contrast enhancement
• convolutional neural networks
• multiple random walkers
• guidance images