Acquiring pairwise noisy-clean training data is challenging. Consequently, some self-supervised denoising methods utilize noisy image pairs as both input and target for network training. However, a major issue with these methods is the gap between the clean images of the input and target. In this paper, we achieve high-quality image denoising by reducing or even eliminating this gap. Our method requires no training data or prior knowledge of the noise distribution. It consists of two lightweight networks that can be trained using only a single noisy test image. Specifically, we propose a random mask-based downsampler that generates multiple pairs of downsampled noisy images, which are similar but distinct. These image pairs serve as the input for the first network, with the mean image of each pair used as the target. This initially reduces the gap between the clean images of the input and target. Particularly, in our method, the clean counterpart of the first network's target (i.e., the mean image) can be obtained. We then train a second network using the mean image as input and its clean counterpart as the target. This effectively eliminates the gap and achieves better denoising results. Extensive experiments demonstrate that our method outperforms in both denoising performance and efficiency.

Infrared and visible image fusion (IVIF) aims to generate fused images with prominent targets and rich scene information. However, in low-light conditions, visible images lose accurate texture and color, reducing their ability to provide detailed scene information for fusion. Existing IVIF methods often overlook illumination degradation and cause color distortion when incorporating infrared information. To address these problems, we propose a novel visually enhanced IVIF method tailored for low-light environments. Our method combines low-light image enhancement (LLIE) and IVIF into a single module. First, we adaptively enhance the low-light visible image, ensuring rich texture and color for fusion. Additionally, we introduce a three-channel fusion coefficient map to transform infrared information into visible image, preventing color distortion and highlighting key targets while maintaining details of the fused image. Since infrared and visible images are from different modalities, we map them into the same high-dimensional feature space. We then propose the feature difference to integrate complementary information, producing a fused image with complete content and no redundancy. Notably, our method is zero-shot, requiring only a pair of test infrared and visible images for training. This better meets the complexity of IVIF in various low-light scenes. Extensive experiments show that in low-light conditions, our method surpasses other state-of-the-art (SOTA) methods by providing more natural colors, richer textures, and better alignment with human visual perception.

Recent advances such as OpenAI-o1 and DeepSeek R1 have demonstrated the potential of Reinforcement Learning (RL) to enhance reasoning abilities in Large Language Models (LLMs). While open-source replication efforts have primarily focused on mathematical and coding domains, methods and resources for developing general reasoning capabilities remain underexplored. This gap is partly due to the challenge of collecting diverse and verifiable reasoning data suitable for RL. We hypothesize that logical reasoning is critical for developing general reasoning capabilities, as logic forms a fundamental building block of reasoning. In this work, we present SynLogic, a data synthesis framework and dataset that generates diverse logical reasoning data at scale, encompassing 35 diverse logical reasoning tasks. The SynLogic approach enables controlled synthesis of data with adjustable difficulty and quantity. Importantly, all examples can be verified by simple rules, making them ideally suited for RL with verifiable rewards. In our experiments, we validate the effectiveness of RL training on the SynLogic dataset based on 7B and 32B models. SynLogic leads to state-of-the-art logical reasoning performance among open-source datasets, surpassing DeepSeek-R1-Distill-Qwen-32B by 6 points on BBEH. Furthermore, mixing SynLogic data with mathematical and coding tasks improves the training efficiency of these domains and significantly enhances reasoning generalization. Notably, our mixed training model outperforms DeepSeek-R1-Zero-Qwen-32B across multiple benchmarks. These findings position SynLogic as a valuable resource for advancing the broader reasoning capabilities of LLMs. We will open-source both the data synthesis pipeline and the SynLogic dataset.

This paper presents a novel zero-shot method for jointly denoising and enhancing real-word low-light images. The proposed method is independent of training data and noise distribution. Guided by illumination, we integrate denoising and enhancing processes seamlessly, enabling end-to-end training. Pairs of downsampled images are extracted from a single original low-light image and processed to preliminarily reduce noise. Based on the smoothness of illumination, near-authentic illumination can be estimated from the denoised low-light image. Specifically, the illumi-nation is constrained by the denoised image's brightness, uniformly amplifying pixels to raise overall brightness to normal-light level. We simultaneously restrict the illumi-nation by scaling each pixel of the denoised image based on its intensity, controlling the enhancement amplitude for different pixels. Applying the illumination to the original low-light image yields an adaptively enhanced reflection. This prevents under-enhancement and localized overexpo-sure. Notably, we concatenate the reflection with the illumi-nation, preserving their computational relationship, to ul-timately remove noise from the original low-light image in the form of reflection. This provides sufficient image infor-mation for the denoising procedure without changing the noise characteristics. Extensive experiments demonstrate that our method outperforms other state-of-the-art meth-ods. The source code is available at https://github.com/Doyle59217/ZeroIG.