Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain

We propose a novel approach for low-light image enhancement. Based on illumination-reflection model, the guided image filter is employed to extract the illumination component of the underlying image. Afterwards, we obtain the reflection component and enhance it by nonlinear functions, sigmoid and gamma, respectively. We use the first-order edge-aware constraint in the gradient domain to achieve good edge preserving features of enhanced images and to eliminate halo artefact effectively. Moreover, the resulting images have high contrast and ample details due to the enhanced illumination and reflection component. We evaluate our method by operating on a large amount of low-light images, with comparison with other popular methods. The experimental results show that our approach outperforms the others in terms of visual perception and objective evaluation

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior

In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications

Empowering Low-Light Image Enhancer through Customized Learnable Priors

Deep neural networks have achieved remarkable progress in enhancing low-light images by improving their brightness and eliminating noise. However, most existing methods construct end-to-end mapping networks heuristically, neglecting the intrinsic prior of image enhancement task and lacking transparency and interpretability. Although some unfolding solutions have been proposed to relieve these issues, they rely on proximal operator networks that deliver ambiguous and implicit priors. In this work, we propose a paradigm for low-light image enhancement that explores the potential of customized learnable priors to improve the transparency of the deep unfolding paradigm. Motivated by the powerful feature representation capability of Masked Autoencoder (MAE), we customize MAE-based illumination and noise priors and redevelop them from two perspectives: 1) \textbf{structure flow}: we train the MAE from a normal-light image to its illumination properties and then embed it into the proximal operator design of the unfolding architecture; and m2) \textbf{optimization flow}: we train MAE from a normal-light image to its gradient representation and then employ it as a regularization term to constrain noise in the model output. These designs improve the interpretability and representation capability of the model.Extensive experiments on multiple low-light image enhancement datasets demonstrate the superiority of our proposed paradigm over state-of-the-art methods. Code is available at https://github.com/zheng980629/CUE.Comment: Accepted by ICCV 202

Perbaikan Citra Gelap dan Pembesaran Objek Citra Menggunakan Gradient Based Low-Light Image Enhancement dan Rational Ball Cubic B-Spline With Genetic Algorithm

Citra yang berkontras rendah (gelap) serta citra yang memiliki objek tidak jelas menyebabkan objek pada citra sulit diidentifikasi baik secara sistem ataupun oleh pengamat. Salah satu solusi dalam mengatasi masalah ini dengan menambahkan proses untuk perbaikan kualitas citra (image enhancement). Perbaikan kualitas citra berbasis gradient mempunyai hasil yang cukup baik karena unsur gradient dipercaya lebih sensitif terhadap sistem visual manusia dengan metode Gradient Based Low-Light Image Enhancement mampu memperbaiki kualitas citra gelap dan metode Rational Ball Cubic B-Spline with Genetic Algorithm untuk memperbesar objek tidak jelas pada citra terbukti memiliki hasil yang bagus dalam implementasinya. Pengujian untuk metode Gradient Based menggunakan metode SSIM dalam membandingkan citra hasil dan citra raw yang menghasilkan rata-rata nilai SSIM baik yaitu 7.0077 sedangkan pengujian untuk metode Rational Ball menggunakan persepsi dari user dengan hasil yang relatif lebih bai