Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images

Abstract

Due to the poor lighting condition and limited dynamic range of digital imaging devices, the recorded images are often under-/over-exposed and with low contrast. Most of previous single image contrast enhancement (SICE) methods adjust the tone curve to correct the contrast of an input image. Those methods, however, often fail in revealing image details because of the limited information in a single image. On the other hand, the SICE task can be better accomplished if we can learn extra information from appropriately collected training data. In this work, we propose to use the convolutional neural network (CNN) to train a SICE enhancer. One key issue is how to construct a training dataset of low-contrast and high-contrast image pairs for end-to-end CNN learning. To this end, we build a large-scale multi-exposure image dataset, which contains 589 elaborately selected high-resolution multi-exposure sequences with 4,413 images. Thirteen representative multi-exposure image fusion and stack-based high dynamic range imaging algorithms are employed to generate the contrast enhanced images for each sequence, and subjective experiments are conducted to screen the best quality one as the reference image of each scene. With the constructed dataset, a CNN can be easily trained as the SICE enhancer to improve the contrast of an under-/over-exposure image. Experimental results demonstrate the advantages of our method over existing SICE methods with a significant margin.

Progressive Retinex

“…So we use the PSNR and SSIM to assess the enhanced result for this dataset. MIP dataset [4] contains 589 high-resolution multiexposure sequences with 4,413 images. And the ground truth of this dataset is derived from several representative multi-exposure image fusion and stack-based high dynamic range imaging algorithms [27] [2].…”

“…This implies that our method has better generalization for various scenes than other prior/assumption based methods. Next, we compare our method with CNN-based single image contrast enhancer [4] on MPI dataset. We achieve a slightly better result (PSNR: 19.77, FSIM [39]: 0.9456) than the method in [4] (PSNR: 19.77, FSIM: 0.9347) for under-exposure image enhancement.…”

“…Besides, we present a fully pointwise convolutional unit to select the representative pixels related to illumination or noise, and thus model the statistics of these pixels as the regularity ρ( * ) in Eq. (3) and (4). Note that our method is quite [37], which includes a pixel scrambling step and aims at achieving a lightweight CNN.…”

Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion

“…For the multi-exposure image fusion task, we aim to combine one under-exposed image with an over-exposed one to obtain a photo-realistic natural image. We use the datasets from [68] for training and testing. The paper [68] provides the images with seven exposure levels, and we choose the first and sixth levels as the under-exposed and over-exposed images, respectively.…”

“…We use the datasets from [68] for training and testing. The paper [68] provides the images with seven exposure levels, and we choose the first and sixth levels as the under-exposed and over-exposed images, respectively. Some examples of the images are shown in Fig.…”