Super-Pixel Guided Low-Light Images Enhancement with Features Restoration

Abstract

Dealing with low-light images is a challenging problem in the image processing field. A mature low-light enhancement technology will not only be conductive to human visual perception but also lay a solid foundation for the subsequent high-level tasks, such as target detection and image classification. In order to balance the visual effect of the image and the contribution of the subsequent task, this paper proposes utilizing shallow Convolutional Neural Networks (CNNs) as the priori image processing to restore the necessary image feature information, which is followed by super-pixel image segmentation to obtain image regions with similar colors and brightness and, finally, the Attentive Neural Processes (ANPs) network to find its local enhancement function on each super-pixel to further restore features and details. Through extensive experiments on the synthesized low-light image and the real low-light image, the experimental results of our algorithm reach 23.402, 0.920, and 2.2490 for Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Natural Image Quality Evaluator (NIQE), respectively. As demonstrated by the experiments on image Scale-Invariant Feature Transform (SIFT) feature detection and subsequent target detection, the results of our approach achieve excellent results in visual effect and image features.

FDMLNet: A Frequency-Division and Multiscale Learning Network for Enhancing Low-Light Image

“…Swarm intelligence algorithms, image decomposition, Rayleigh distribution, and other technologies [ 31 , 32 , 33 ] were hired to optimize the previous HE-based approaches. Additionally, gamma, S-shape, logarithmic, and other improved nonlinear functions [ 34 , 35 , 36 ] also can restore inherent color and details of excessively dark images through pixel transformation. Unfortunately, these above-listed methods either amplify noise or yield improper exposure.…”

Efficient Color Quantization Using Superpixels

“…New modifications to the SLIC algorithm are constantly appearing that improve the quality of superpixel segmentation, for example SLIC++ [22] used for semi-dark images. Superpixels provide a useful framework for image processing operations such as low-light image enhancement [23], image segmentation [24], saliency detection [25], dimensional reduction in hyperspectral image classification [26], and full-reference image quality assessment [27]. Recently, superpixel algorithms have also been applied to video sequences, for example, to avoid a dimensional explosion problem [28].…”

Joint-Prior-Based Uneven Illumination Image Enhancement for Surface Defect Detection

“…Recently, convolutional neural networks (CNNs) have been widely applied in image processing, including illumination correction [3,4]. Such models learn the relationship between image pairs with uneven and normal illumination via an end-to-end approach.…”