text: We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-of-the-art approaches for a variety of natural images.
  @cite_9: 
  @cite_23: A method for fusing multi-exposure images of a static scene taken by a stationary camera into an image with maximum information content is introduced. The method partitions the image domain into uniform blocks and for each block selects the image that contains the most information within that block. The selected images are then blended together using monotonically decreasing blending functions that are centered at the blocks and have a sum of 1 everywhere in the image domain. The optimal block size and width of the blending functions are determined using a gradient-ascent algorithm to maximize information content in the fused image.
  @cite_7: The authors present an extension to the pyramid approach to image fusion. The modifications address problems that were encountered with past implementations of pyramid-based fusion. In particular, the modifications provide greater shift invariance and immunity to video noise, and provide at least a partial solution to the problem of combining components that have roughly equal salience but opposite contrasts. The fusion algorithm was found to perform well for a range of tasks without requiring adjustment of the algorithm parameters. Results were remarkably insensitive to changes in these parameters, suggesting that the procedure is both robust and generic. A composite imaging technique is outlined that may provide a powerful tool for image capture. By fusing a set of images obtained under restricted, narrowband, imaging conditions, it is often possible to construct an image that has enhanced information content when compared to a single image obtained directly with a broadband sensor. >
summary: Many algorithms have been proposed over the years for exposure fusion. However, the main idea remains the same in all the algorithms. The algorithms compute the weights for each image either locally or pixel wise. The fused image would then be the weighted sum of the images in the input sequence. Burt @cite_7 performed a Laplacian pyramid decomposition of the image and the weights are computed using local energy and correlation between the pyramids. Use of Laplacian pyramids reduces the chance of unnecessary artifacts. Goshtasby @cite_23 take non-overlapping blocks with highest information from each image to obtain the fused result. This is prone to suffer from block artifacts. Mertens @cite_9 perform exposure fusion using simple quality metrics such as contrast and saturation. However, this suffers from hallucinated edges and mismatched color artifacts.