PESiT: Progressive Joint Enhancement and Blind Super-Resolution for Low-Light and Low-Resolution Images Under Total Variation Constraints

Abstract

Traditional enhancement techniques can improve the contrast of low-light and low-resolution images, but they fail to recover their resolution. Conversely, traditional super-resolution (SR) algorithms can enhance resolution but not restore contrast. To address this issue, a novel progressive joint enhancement and SR tactic, named PESiT, is proposed to synchronously improve contrast and resolution in low-light and low-resolution images. PESiT comprises an enhanced multi-scale Retinex module followed by a blind SR module with regularization optimization. In the first module, the common logarithm is replaced with an S-function to expand the intensity distribution of images and prevent color inversion. In the latter module, those merits of reconstruction- and learning-based tactics are combined to tackle various unknown degradations by imposing consistency constraints on high- and low-resolution image pairs. Extensive experiments on public datasets demonstrate the robustness and superiority of PESiT in processing low-light and low-resolution images under various scenarios. Compared with state-of-the-art techniques, PESiT achieves superior performance, e.g., the highest peak signal-to-noise ratio, structural similarity index, feature similarity index, and the lowest learned perceptual image patch similarity, highlighting its validity in achieving optimal image quality improvements.

Keywords

• Image enhancement
• low-light enhancement
• low-resolution image
• super-resolution