An Unsupervised Image Enhancement Method Based on Adaptation Region Divisions

Abstract

This paper presents a novel image enhancement method that integrates traditional image processing techniques with deep learning frameworks. Initially, images are transformed from the red, green and blue (RGB) color space to the Lab color space, and the luminance component (L) is extracted to quantify texture. Subsequently, texture complexity is assessed using features derived from the gray-level co-occurrence matrix (GLCM), including contrast, correlation, homogeneity, and energy. These features are weighted to compute an overall texture complexity score, which facilitates the segmentation of the image into distinct regions. Regions characterized by simple textures are aggregated into larger segments, whereas regions with complex textures are subdivided into smaller segments. Following segmentation, histogram equalization is applied along with noise reduction and image enhancement via a convolutional autoencoder model. The model extracts relevant features and reduces dimensionality in the encoder phase, and reconstructs the image through the decoder. This methodology effectively preserves semantic information and enhances image clarity. Some experiments are conducted using the ExDark dataset, which comprises twelve categories, and the enhancement results are quantitatively evaluated using image quality metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), learned perceptual image patch similarity (LPIPS), and neural image quality evaluator (NIQE). Experimental results demonstrate that the proposed method significantly surpasses existing enhancement techniques in terms of image quality and visual perception, thereby affirming its efficacy in improving the visual quality and detail of low-light images. The implementation code will be made publicly available at: https://github.com/Winnie0320/Image-Enhancement-Method.