Abstract
Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under challenging illumination conditions. Even though the significant progress has been made for enhancing the poor visibility, the intrinsic noise amplified in low-light areas still remains as an obstacle for further improvement in visual quality. In this paper, a novel and simple method for low-light image enhancement is proposed. Specifically, the subspace, which has an ability to separately reveal illumination and noise, is constructed from a group of similar image patches, so-called volume, at each pixel position. Based on the principal energy analysis onto this volume-based subspace, the illumination component is accurately inferred from a given image while the unnecessary noise is simultaneously suppressed. This leads to clearly unveiling the underlying structure in low-light areas without loss of details. Experimental results show the efficiency and robustness of the proposed method for low-light image enhancement compared to state-of-the-art methods.
Highlights
With the rapid development of mobile devices equipped by cameras, especially smartphones, a vast amount of photos are taken and shared everyday
Based on the general principle that similar image patches contain similar lighting conditions, we propose to exploit the principal energy of the subspace, which is generated from a group of similar image patches in a least square sense, so-called volume, as the illumination component
EXPERIMENTAL RESULTS To demonstrate the efficiency and robustness of the proposed method, we evaluate the performance of our model based on two benchmark datasets, i.e., NASA [22] (25 images) and HDR [23] datasets, which have been most widely employed for this task in literature
Summary
With the rapid development of mobile devices equipped by cameras, especially smartphones, a vast amount of photos are taken and shared everyday. Loss of details and color distortions in such degraded images lead to the significant performance drop in further applications of computer vision such as object detection, tracking, and segmentation, which demand high-quality inputs for precise results. To tackle this problem, diverse methods for low-light image enhancement have been introduced for last decades, which can be mainly categorized into twofold: statistical information-based approach and decomposition-based approach. Diverse methods for low-light image enhancement have been introduced for last decades, which can be mainly categorized into twofold: statistical information-based approach and decomposition-based approach The former stretches the dynamic range of the. Various optimization techniques, e.g., variational framework [4], direction minimization [5], etc., have been adopted to accurately estimate the illumination component by minimizing the difference between target and estimated results with
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