## 1 Introduction  
As imaging technology advances, image quality has improved significantly over the past few decades. The ultimate goal is to present images of real scenes that are directly visible to the human eye. Although several aspects of visual sensing capabilities, such as image resolution, color accuracy, and optical sharpness, have been enriched, the luminance range of current imaging devices is still limited compared to the human visual system. This is mainly due to the adaptability of visual perception to large illumination changes, but photosensitive elements can only collect a fixed amount of light. Moreover, although high dynamic range (HDR) cameras can be used for directly capturing HDR images, they can still not cover the entire dynamic range of a scene. Therefore, it is essential to generate an HDR to increase the contrast of image content to achieve colorful scene representation. The dynamic range of an image represents the ratio between the maximum and minimum measurable light intensities. Low dynamic range (LDR) images record 8 bits of information for each channel of each pixel. In general, it lacks sufficient quantization levels to cover wide intensity variations. The presented dynamic range is much lower than the real scene and cannot meet our needs for image visual effects. Several approaches [1; 2; 3; 4] have been proposed for reconstructing HDR images that can record richer light and dark changes. An image acquisition technique [1] is to take multiple images with different exposure values in the same scene, also called bracketed exposure images, and combine these images into an HDR image. Although this technique can effectively produce HDR images, it may be affected by scene changes in dynamic scenes, making it difficult to achieve desirable results. It is also limited by the difficulty in obtaining bracketed exposure images and cannot be directly applied to existing images. Several approaches [2; 4] focused on solving the problem of object shaking and artifacts when shooting bracketed exposure images. However, the techniques of using multiple images are still inconvenient in data acquisition and application. One way to generate an HDR is to extend a single LDR image to its HDR counterpart. The so-called LDR2 HDR technique utilizes an inverse tone mapping operator to extend the dynamic range [5; 6; 7]. For the last decade, convolutional neural networks (CNNs) have been intensively studied in LDR2 HDR algorithms [8; 9]. These reconstruction approaches for HDR images have greatly improved their practicality and improved the problems of previous methods. However, reconstructing an HDR image from a single LDR image is always a challenging task. In this paper, we propose a two-stage approach for reconstructing an HDR image from an LDR image using histogram learning. In the first stage, we first perform a preliminary dynamic range extension on the input LDR image to an extended dynamic range (EDR) image. Then, we use a neural network to learn the changes from EDR image histograms to HDR image histograms. Next, we predict an HDR histogram of the EDR image through cumulative histogram learning. Finally, we use the predicted HDR histogram to perform histogram matching on the EDR image to generate the first-stage HDR output image. In the second stage, the final HDR image is reconstructed using reinforcement learning with pixel-level rewards for local consistency adjustment. Hence, we can obtain favorable HDR image quality using the proposed approach. For applications, several vision systems require an HDR camera for capturing high-quality images including autonomous mobile robots, autonomous guided vehicles, smart traffic management systems, etc.