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.

No abstract

No abstract

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.