This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demoireing is a difcult task of removing moire patterns from an image to reveal an underlying clean image. The challenge was divided into two tracks. Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image. Track 2 focused on the burst demoireing problem, where a set of degraded moire images of the same scene were provided as input, with the goal of producing a single demoired image as output. The methods were ranked in terms of their fidelity, measured using the peak signal-to-noise ratio (PSNR) between the ground truth clean images and the restored images produced by the participants' methods. The tracks had 142 and 99 registered participants, respectively, with a total of 14 and 6 submissions in the final testing stage. The entries span the current state-of-the-art in image and burst image demoireing problems.

We propose an end-to-end unpaired learning approach to screen-shot image demoiréing based on cyclic moiré learning. The proposed cyclic moiré learning algorithm consists of the moiréing network and the demoiréing network. The moiréing network generates moiré images to construct a pseudo-paired set of moiré and clean images. Then, the demoiréing network is trained in a supervised manner using the generated pseudo-paired dataset to remove moiré artifacts. In the moiréing network, the moiré generation is separately learned as global pixel intensity degradation and moiré pattern generation for more realistic moiré artifact generation. Furthermore, the moiréing network and the demoiréing network are integrated together to be trained in an end-to-end manner. Experimental results on different datasets demonstrate that the proposed algorithm significantly outperforms state-of-the-art unsupervised demoiréing algorithms as well as image restoration algorithms.

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We propose a learning-based low-light image enhancement algorithm, called the histogram-based transformation function estimation network (HTFNet), that estimates transformation functions using the histogram of an input image. First, we obtain an attention image that indicates the pixel-wise information on the level of enhancement. Then, the proposed HTFNet generates the transformation functions by exploiting both the spatial and statistical information of the input image by combining two feature maps extracted from the input image and its histogram. Finally, the enhanced images are obtained via channel-wise intensity transformation. Experimental results show that the proposed algorithm provides higher image quality compared with the state-of-the-art algorithms.

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We propose an infrared and visible image fusion algorithm using bimodal transformers. First, the proposed algorithm extracts multiscale features of the input infrared and visible images. Then, we develop the bimodal transformers that refine the extracted features by estimating their irrelevance maps to exploit the complementary information of the source images. Finally, we develop a reconstruction block that generates the fusion result by merging the refined features in the frequency domain to exploit the global information of the source images. Experimental results show that the proposed algorithm outperforms state-of-the-art infrared and visible image fusion algorithms on several datasets.

We propose a single-shot high dynamic range (HDR) imaging algorithm with row-wise varying exposures in a single raw image based on a deep convolutional neural network (CNN). We first convert a raw Bayer input image into a radiance map by calibrating rows with different exposures, and then we design a new CNN model to restore missing information at the under-and over-exposed pixels and reconstruct color information from the raw radiance map. The proposed CNN model consists of three branch networks to obtain multiscale feature maps for an image. To effectively estimate the high-quality HDR images, we develop a robust loss function that considers the human visual system (HVS) model, color perception model, and multiscale contrast. Experimental results on both synthetic and captured real images demonstrate that the proposed algorithm can achieve synthesis results of significantly higher quality than conventional algorithms in terms of structure, color, and visual artifacts.INDEX TERMS Spatially varying exposure (SVE) image, high dynamic range (HDR) imaging, convolutional neural network (CNN), and human visual system (HVS).