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Image decolorization is a task aiming to transform a color image to a grayscale one and is a dimension reduction process which inevitably suffers from information loss. The general goal of image decolorization is to preserve the color contrast of the color image. According to human visual study, exposure affects the human visual perception, and low-exposure areas or over-exposure areas will first attract the sense of sight. In addition, exposure also affects the contrast of the image, the contrast of low-exposure areas and over-exposure areas often cannot be well shown. Thus, the exposure should be taken into account in the process of image decolorization. Traditional local methods are not accurate enough to process local pixel blocks which may tend to cause local artifacts, while traditional global methods cannot greatly deal with local color blocks, which are usually time consuming too. Besides, the traditional image decolorization method usually uses the low-level features of an image. In this paper, the convolutional neural network is used to learn high-level abstract features of the image. We design a new convolutional neural-network framework with a local feature network and a rough classifier, which can learn the local semantic features and distinguish the different exposure conditions of color images. It is possible to learn the mapping model between input–output image pairs, which can generate better results in terms of color contrast preservation and exposure adjustment. Experiments indicate that our method does better in terms of color contrast preservation and exposure adjustment than the state of the art.