Low-light Image Enhancement (LLIE) aims to rectify inadequate illumination conditions and achieve superior visual quality in images, which plays a pivotal role in the domain of low-level computer vision. Due to poor illumination in images, many high-frequency details are obscured, which leads to an uneven distribution of low- and high-frequency information. However, most existing LLIE methods do not pay special attention to the restoration of high-frequency detail information and some challenging-to-recover areas in images. To address this issue, we propose a novel progressive prompt-driven LLIE framework with frequency aware learning, through a two-stage coarse-to-fine learning mechanism. Specifically, the proposed method fully utilizes both the specially designed brightness-aware prompt and detail-aware prompt on the prior trained model, to achieve an excellent enhanced image that exhibits more natural brightness and richer detail information. Furthermore, the proposed frequency aware learning objective can adaptively adjust the contribution of individual pixels for image reconstruction based on the statistics of high- and low-frequency features, which enables the network to focus on learning intricate details and other challenging areas in low-light images. Extensive experimental results demonstrate the effectiveness of the proposed method, achieving superior performances to state-of-the-art methods on representative real-world and synthetic datasets. Our source code is available athttps://github.com/MSL502/PPFAL.

Low-light image enhancement aims to reconstruct images with insufficient illumination into visually appealing representations with natural brightness. While most existing methods tend to focus on enhancing illumination, they often overlook the restoration of finer details in the enhanced image. Moreover, these methods do not adequately address the varying degradation levels observed in different regions of the image. In this study, we present a gradient and brightness guided low-light image enhancement framework, which can simultaneously augment the detail and illumination during the enhancement process. Our approach involves extracting gradient information from gamma-corrected images, which offers a remarkable advantage in preserving edge details compared to direct extraction from degraded images. To further refine the enhancement process and adaptively adjust the difficulty of samples, thereby boosting learning efficiency, we introduce an attention-based self-paced learning strategy. This strategy assigns different gradient and brightness weights based on the degradation levels within different image regions. Extensive experiments demonstrate the superiority of our proposed method over state-of-the-art approaches. The code is available at https://github.com/MSL502/GBASPL.

In real-world applications, image degeneration caused by adverse weather is always complex and changes with different weather conditions from days and seasons. Systems in real-world environments constantly encounter adverse weather conditions that are not previously observed. Therefore, it practically requires adverse weather removal models to continually learn from incrementally collected data reflecting various degeneration types. Existing adverse weather removal approaches, for either single or multiple adverse weathers, are mainly designed for a static learning paradigm, which assumes that the data of all types of degenerations to handle can be finely collected at one time before a single-phase learning process. They thus cannot directly handle the incremental learning requirements. To address this issue, we made the earliest effort to investigate the continual all-in-one adverse weather removal task, in a setting closer to real-world applications. Specifically, we develop a novel continual learning framework with effective knowledge replay (KR) on a unified network structure. Equipped with a principal component projection and an effective knowledge distillation mechanism, the proposed KR techniques are tailored for the all-in-one weather removal task. It considers the characteristics of the image restoration task with multiple degenerations in continual learning, and the knowledge for different degenerations can be shared and accumulated in the unified network structure. Extensive experimental results demonstrate the effectiveness of the proposed method to deal with this challenging task, which performs competitively to existing dedicated or joint training image restoration methods. Our code is available athttps://github.com/xiaojihh/CL_all-in-one.

Image dehazing is a pivotal preliminary step in the advancement of robust intelligent surveillance system. However, it is an extremely challenging ill-posed problem, as it faces severe information degradation when accurately restoring the clean image from its haze-polluted counterpart. This paper proposes a novel Progressive Negative Enhancing (PNE) contrastive learning mechanism to fully exploit various types of negative information, thereby facilitating the traditional positive-oriented objective function for image dehazing. The proposed method can progressively update the negative samples during model training, to steadily squeeze the restored image towards its desired clean target from various directions. Furthermore, considering the image dehazing task as a many-to-one feature mapping problem, we also make an early effort to enhance the robustness of the dehazing model under variational haze densities. Specifically, a novel density-variational dehazing network is proposed to be optimized under the consistency-regularized framework using the proposed PNE learning mechanism. The consistency regularization ensures consistent output given multi-level degraded hazy images, thereby significantly enhancing the robustness of the model in dealing with various hazy scenarios. Extensive experiments demonstrate that the proposed method exhibits superior performance over existing state-of-the-art methods. It achieves average PSNR boosts of 0.60dB, 0.28dB and 0.82dB on dehazing, deraining and desnowing tasks, respectively. The source code is available athttps://github.com/YanLi-LY/PNE-Net.

Single image dehazing as a fundamental low-level vision task, is essential for the development of robust intelligent surveillance system. In this paper, we make an early effort to consider dehazing robustness under variational haze density, which is a realistic while under-studied problem in the research filed of singe image dehazing. To properly address this problem, we propose a novel density-variational learning framework to improve the robustness of the image dehzing model assisted by a variety of negative hazy images, to better deal with various complex hazy scenarios. Specifically, the dehazing network is optimized under the consistency-regularized framework with the proposed Contrast-Assisted Reconstruction Loss (CARL). The CARL can fully exploit the negative information to facilitate the traditional positive-orient dehazing objective function, by squeezing the dehazed image to its clean target from different directions. Meanwhile, the consistency regularization keeps consistent outputs given multi-level hazy images, thus improving the model robustness. Extensive experimental results on two synthetic and three real-world datasets demonstrate that our method significantly surpasses the state-of-the-art approaches.