TM-Net: A Neural Net Architecture for Tone Mapping

Abstract

Tone mapping functions are applied to images to compress the dynamic range of an image, to make image details more conspicuous, and most importantly, to produce a pleasing reproduction. Contrast Limited Histogram Equalization (CLHE) is one of the simplest and most widely deployed tone mapping algorithms. CLHE works by iteratively refining an input histogram (to meet certain conditions) until convergence, then the cumulative histogram of the result is used to define the tone map that is used to enhance the image. This paper makes three contributions. First, we show that CLHE can be exactly formulated as a deep tone mapping neural network (which we call the TM-Net). The TM-Net has as many layers as there are refinements in CLHE (i.e., 60+ layers since CLHE can take up to 60 refinements to converge). Second, we show that we can train a fixed 2-layer TM-Net to compute CLHE, thereby making CLHE up to 30× faster to compute. Thirdly, we take a more complex tone-mapper (that uses quadratic programming) and show that it too can also be implemented — without loss of visual accuracy—using a bespoke trained 2-layer TM-Net. Experiments on a large corpus of 40,000+ images validate our methods.

Keywords

• Histogram Equalization
• Contrast Limited Histogram Equalization
• tone mapping
• contrast enhancement
• unrolling