使用穩健視覺分層之隨插即用低光影像增強技術

Abstract

低光的攝影條件會使得影像品質下降，造成細節損失、對比度降低等問題。本論文提出了一種全新基於 Retinex 理論的低光影像增強方法，能夠準確地將輸入影像進行視覺拆分為反射層和照明層，而能更有效地處理低光問題。透過調整照明層的亮度和對比度，我們可以顯著改善影像的視覺效果。然而，由於影像分解是一個最佳化問題，我們在其框架中引入了適當的限制條件以提升結果的可靠性。 為了實現理想的 Retinex 分解，我們引入了非凸的 $L_p$ 範數，對照明層中使用收縮映射，以進一步提升精確性和穩定性，此外，我們也利用即插即用(Plug-and-Play)技術引入邊緣保持濾波器來改進照明層的效果。在反射層中，我們採用了基於變異數和影像梯度的加權方法，以有效抑制雜訊並保留更多細節。 為了提高計算效率，我們採用了交替方向乘數法（ADMM）來處理最佳化問題。實驗結果顯示，該方法在多個具有挑戰性的低光影像測試庫中取得了優異的性能。與當前最先進的方法相比，我們的方法能夠更有效地增強影像亮度，並在主觀、客觀等多種影像品質評估中有出色的表現，為低光影像增強提供了一個全新而高效的解決方案。

Low-light photography conditions degrade image quality. This study proposes a novel Retinex-based low-light enhancement method to decompose an input image into reflectance and illumination correctly. Subsequently, we can improve the viewing experience by adjusting the illumination using intensity and contrast enhancement. Because image decomposition is a highly ill-posed problem, constraints must be appropriately imposed on the optimization framework. To meet the criteria of ideal Retinex decomposition, we design a nonconvex $L_p$ norm and apply shrinkage mapping to the illumination layer. In addition, edge-preserving filters are introduced using the plug-and-play technique to improve illumination. Pixel-wise weights based on variance and image gradients are adopted to suppress noise and preserve details in the reflectance layer. We choose the alternating direction method of multipliers (ADMM) to solve the problem efficiently. Experimental results on several challenging low-light datasets show that our proposed method can more effectively enhance image brightness than state-of-the-art methods. In addition to subjective observations, the proposed method also achieved competitive performance in objective image quality assessments.