## 2 Related Works  
Several methods [5; 7; 8; 10; 11; 12; 13] have been intensively studied to extend a single LDR image to an HDR image. [PERSON] and [PERSON] [5] presented a bit slicing approach combined with stereo matching algorithms for HDR images reconstructed from multiple exposures. Their approach can achieve comparable bad pixel rates on stereo matching using only the most significant 16 bits of an HDR image encoding per pixel. This approach demonstrated desirable results compared to the previous methods. [PERSON] et al. [7] proposed a reverse tone mapping algorithm for reconstructing the HDR of videos and images from LDR contents for viewing on HDR displays. Their algorithm was efficient to work in real time and no user input was required. Moreover, this method did not produce disturbing artifacts and the visual quality of the HDR output image was good. For learning-based approaches, [PERSON] et al. [10] proposed a fully convolutional hybrid dynamic range autoencoder to predict HDR values in saturated regions to recover the loss of detail. Their approach can predict information lost in saturated image areas to reconstruct an HDR image from a single LDR image. The results demonstrated that this approach can reconstruct visually convincing HDR results in a wide range of situations compared to existing methods. [PERSON] et al. [12] presented a learning-based technique to reconstruct an HDR image by recovering the saturated pixels of an input LDR image. They proposed a feature masking mechanism to reduce the influences of saturated areas and adopted a perceptual loss function to synthesize images with visually pleasant textures. Their approach can address ambiguity during training, checkerboard, and halo artifacts. Inspired by the LDR image formation pipeline, [PERSON] et al. [11] proposed a technique to reverse the steps of LDR image formation to gradually restore HDR images that are close to the real scene. This approach proposed to learn three specialized CNNs to reverse three steps including dynamic range clipping, non-linear mapping from a camera response function, and quantization. Alternatively, a data-driven learning method [14] was proposed to reconstruct information lost from the original image due to quantization, dynamic range cropping, tone mapping, or gamma correction. This technique reconstructed an HDR image from an LDR image using deep CNNs. Lightweight deep neural network models have increasingly attracted much attention due to the demand for real-time processing capabilities. [PERSON] et al. [13] proposed a lightweight CNN to reconstruct an HDR image from a single LDR image. They used up-sampling blocks in the decoder to alleviate artifacts while maintaining computational speed. Their approach can recover the lost information in saturated regions and reconstruct HDR images of high quality. [PERSON] and [PERSON] [15] presented an approach to improve HDR reconstruction for legacy content through camera pipeline modeling. They proposed a lightweight deep neural network (DNN) for legacy standard dynamic range (SDR) images containing unreproducible historical scenes. The results showed that this approach can achieve appealing performance with minimal computational cost. [PERSON] et al. [16] presented a brightness-adaptive model based on deep learning to produce an HDR image for one single LDR image. They proposed an inverse tone mapping model based on brightness adaptive kernel prediction. The LDR image was first convolved with an adaptive kernel and then reweighted to obtain HDR results. The results showed that this approach outperforms the previous methods. To simultaneously solve the image super-resolution problem, [PERSON] et al. [17] proposed a joint image tone mapping and super-resolution framework using a multi-purpose CNN structure. This approach can reconstruct fine details by decomposing the input image and focusing on the low-frequency and high-frequency layers. Moreover, they applied location-variant operations to enhance local contrast.