The recent developments in the field of low-light image processing and enhancement have been marked by innovative approaches aimed at overcoming the challenges posed by low visibility and noise in images captured under such conditions. A significant trend is the shift towards methods that not only improve image visibility for human perception but also enhance the performance of recognition models and other downstream tasks. This includes the development of global and pixelwise optimization techniques, the integration of natural language supervision for more flexible and descriptive image enhancement, and the application of frequency-based frameworks for targeted enhancement of task-relevant information. Additionally, there is a growing emphasis on the efficiency and robustness of these methods in real-world scenarios, with advancements in lightweight neural networks and the incorporation of multi-modality data for improved accuracy and robustness in challenging environments.

Noteworthy papers include:

• A novel low-light image enhancement method that improves recognition model performance without retraining, through global and pixelwise optimization.
• SpikeCLIP, a spike-to-image reconstruction framework that leverages textual descriptions and unpaired datasets for enhanced texture details and luminance balance.
• The IAC algorithm, which introduces an image-adaptive Cartesian coordinate system for efficient and state-of-the-art photography processing.
• NaLSuper, a network that utilizes natural language supervision and textual guidance for low-light image enhancement, offering a new paradigm based on visual and textual feature alignment.
• A frequency-based framework for robust low-light human pose estimation, focusing on task-relevant information through dynamic illumination correction and low-rank denoising.
• BrightVO, a Transformer-based visual odometry model that integrates IMU data for improved accuracy and robustness in low-light conditions.
• FLOL+, a lightweight neural network for fast and efficient low-light image enhancement, achieving state-of-the-art results on real scenes datasets.