Study on Unsupervised Image Dehazing and Low-light Image Enhancement Algorithms Based on Luminance Adjustment

Abstract

Among the degradations of low-quality images,luminance deviations such as brighter or darker images are very common image degradation phenomena.The image enhancement method based on fully supervised learning faces the dilemma that the training data is difficult to obtain or the acquisition cost is too high,and the training data is inconsistent with the application scene.To handle these problems,an unsupervised image dehazing and low-light enhancement algorithm based on luminance adjustment is proposed in this paper.A deep architecture with channel attention and pixel attention mechanism are designed to measure the differences between enhanced images and input low-quality images.A variable quadratic function has been applied to adjust the pixel luminance of the image.Multiple unsupervised losses i.e.,brightness saturation loss,spatial consistency loss,illumination smoothness loss and pseudo-label supervision loss are utilized to alleviate the illumination deviations but to ensure the identity between the enhanced images and the input low-quality images,which efficiently improve the quality of the images.Empirically,an intensity compression strategy is applied for the hazy images to darken the hazy images to have a similar intensity range with low-light images.Thus,the hazy images can be treated equally with low-light images with our deep network to adjust the luminance of the image.For the dehazing task,compared with the second-best method,our method improves the PSNR value for 2.8 dB and SSIM value for 0.01 in RESIDE dehazing dataset.For the low-light enhancement task,our method outperforms the second-best method for 0.56 dB and 0.01 separately measured by PSNR and SSIM in the SICE dataset.The proposed image dehazing and low-light enhancement algorithms can restore high-quality images from hazy images and low-light images.It effectively overcomes the difficulty of acquiring the targeted enhanced data or alleviates the problem of domain gap between training data and application data in the low-level vision tasks,which improves its adaptivity in real applications.

Keywords

• unsupervised learning|luminance adjustment|image dehazing|low-light enhancement|deep learning