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Non-uniform low-light images, characterized by complex lighting conditions, pose a significant challenge in image restoration and enhancement, particularly under backlit scenarios where the high-brightness background obscures the foreground information. Existing methods struggle with the diverse illumination factors across different scenes. To overcome this, we introduce a novel deep reconstruction network designed specifically for enhancing non-uniform low-light images. This network leverages the feature representation of uniform natural images, employing the dark channel of the absorption light scattering model (ALSM) to estimate and enhance facial features. Furthermore, the reconstruction process is optimized through the posterior constraint of uniform image characteristics, leading to superior detail enhancement. Our approach has been rigorously tested on both synthetic and real-world images, demonstrating its effectiveness in addressing the complexities of non-uniform low-light image enhancement. The results illustrate a notable enhancement, boosting PSNR by 3.37dB and SSIM by 0.0579 compared to recent methods, with a particular focus on enhancing facial feature details. The code link is https://github.com/zbysygdsghh/face-relight.

For the first time, this research addresses the notable layout proximity effects induced by stress memorization technique in planer high-k/Metal gate NMOS device systematically, including width effect, different shallow trench spacing effect, and length of diffusion effect. Based on the oxygen diffusion mechanism analysis of layout proximity effects in high-k/Metal gate NMOS device, an optimized process is proposed to suppress the layout dependency. The experiment result indicates that modified low temperature stress memorization technique process can suppress layout dependency efficiently without performance degradation of the devices.