Low-light image enhancement methods based on diffusion models, though effective in improving image quality, often overrely on noise sensitivity and neglect the reconstruction deviations due to the naive up- and down-sampling operations. To address this issue, we propose a novel diffusion model, MWT-Diff, which utilizes multi-layer wavelet transforms to replace up-and down-sampling based on convolutions for extracting high-order features of different scales while mitigating representation degradations. Specifically, MWT-Diff is based on the U-Net architecture; it encodes four local features after the frequency-based down-sampling at each layer and fuses the enhanced four components during the up-sampling process. Additionally, we incorporate global refinement branches to mitigate information loss and employ efficient soft gate aggregation for feature fusion and reconstruction. Extensive quantitative and qualitative experiments demonstrate that our model achieves state-of-the-art performance on benchmark datasets. Code is available at: https://github.com/lalalulao/MWT-Diff.

When restoring low-light images, most methods largely overlook the ambiguity due to dark noise and lack discrimination for region and shape representations, resulting in invalid feature enhancement. In this work, we propose a physically explainable and prior guidance model for low-light image enhancement, termed Dual-Conditional Guidance Sparse Diffusion (DCGSD). Specifically, we introduce an elaborately designed Luminance Structure Guidance Head, which can be easily plugged into the existing diffusion model to emphasize the value of the luminance and structural representation. Furthermore, for reliable noise analysis, we provide a novel Sparse Attention Enhancement Module that is adaptively empowered to exploit the most useful region-to-region dependencies. This dynamic selection makes the diffusion process from dense to sparse, thus improving the efficiency of the reasoning noise distributions. To avoid noise amplification, we further present a Skip Calibration Module, which can be used to refine the local neighborhood that contains noisy and structural information. Extensive experiments have been performed to verify the superiority of the proposed method. DCGSD shows that leveraging dual-conditional guidance can support the diffusion model to produce sharper and more realistic results.

Infrared and visible image fusion is currently an important research direction in the field of multimodal image fusion, which aims to utilize the complementary information between infrared images and visible images to generate a new image containing richer information. In recent years, many deep learning-based methods for infrared and visible image fusion have emerged.However, most of these approaches ignore the importance of semantic information in image fusion, resulting in the generation of fused images that do not perform well enough in human visual perception and advanced visual tasks.To address this problem, we propose a semantic prior knowledge-driven infrared and visible image fusion method. The method utilizes a pre-trained semantic segmentation model to acquire semantic information of infrared and visible images, and drives the fusion process of infrared and visible images through semantic feature perception module and semantic feature embedding module.Meanwhile, we divide the fused image into each category block and consider them as components, and utilize the regional semantic adversarial loss to enhance the adversarial network generation ability in different regions, thus improving the quality of the fused image.Through extensive experiments on widely used datasets, the results show that our approach outperforms current leading algorithms in both human eye visualization and advanced visual tasks.

Infrared and visible image fusion is an important multimodal image processing task that aims to enhance computer vision performance by effectively fusing infrared and visible images. Although in recent years, many deep learning-based methods for infrared and visible image fusion have emerged. Howeve, most of these methods ignore the important role of semantic information in image fusion. Therefore, this paper proposes a semantic priori guided infrared and visible image fusion network called SPGFusion. It uses an adversarial generative network framework based on semantic priors to guide the infrared and visible image fusion process by combining a semantic feature-aware module and semantic generative adversarial loss. Experimental results demonstrate that the SPG-Fusion method yields more visually appealing fusion results and outperform state-of-the-art image fusion algorithms in visual quality and quantitative evaluation. The source code is available at https://github.com/tianzhiya/SPGFusion.

Photographs taken in low-light conditions have a low signal-to-noise ratio and impaired visual quality. We observe that low-light images exhibit a lower signal-to-noise ratio, resulting in a mixture of fine details, textures, and noise, making it challenging to reconstruct small-scale textures in the image. Inspired by this observation, we propose a SNR-guided CNN-Transformer network for high frequency restoration during low light image enhancement. The proposed method first decomposes image into high-frequency and low frequency components by image decomposition module. The low-frequency image is processed by a trainable Low Frequency SNR Perception (LFSP) module, resulting in excellent denoising performance and generating SNR-enhanced images with clearer edge contours. Guided by the low-frequency SNR feature maps, the details and textures of the high-frequency components are enhanced using a combination of transformer networks and convolutional networks, thereby compensating the detail distortions in the high frequency components of the image. The subjective and objective experiments demonstrate that our proposed method outperforms existing approaches in terms of detail and structure preservation.

Dark Image Enhancement (DIE) aims to improve contrast and restore details for captured images under low illumination. Currently, traditional Transformer methods have achieved significant performance in the DIE problem; however, all-pairs correlation computation is redundant in learning key properties and restoring high-order representations. To alleviate this problem, we introduce a Channel-wise Guidance Sparse Transformer framework, namely CGSformer, which not only adaptively selects the key channel-wise representations through a threshold operator, but also keeps the most useful self-attention values for feature restoration guided by the selected information. Besides, we introduce a Bidirectional Gate Feed-Forward (BGFF) network to aggregate features to better facilitate high-quality image reconstruction. The experiments are conducted on representative datasets, showing that our CGSformer consistently achieves state-of-the-art performance on widely used benchmarks.

In the low light image enhancement, single exposure images contains a limited dynamic range, which hinders the restoration of contrast and texture. To address these problems, we propose a multi exposure generation and fusion method (MEGF) which simulates multi exposure images and perform feature fusion and enhancement on these images. First, we propose a Multi-Exposure Generation (MEG) method, which constructs the Gaussian Distribution for each exposure level based on multi exposure datasets. MEG can generate images with different exposure levels based on the constructed distribution. Then, the Perceptual Importance based Multi-Exposure Feature Enhancement (PIMEFE) block is developed to fuse the feature of generated multi exposure images using VGG-16 network. Before fusion, the generated images are input to Multi Scale Recursive Feature Enhancement (MSRFE) blocks and obtain the denoised and enhanced features. Finally, the fused feature are input to Curve Adjustment (CA) block for fine tuning and provide the color enhancement on fusion features. We propose the Multiple Exposure Recursive Fusion (MERF) block which estimates the adjusting factors for CA block. Experimental results demonstrate that our method outperforms other techniques in both subjective and objective evaluations on real and synthetic datasets.

In low-light images, contrast and brightness are corrupted, making it difficult to accurately percept detail and edge information with the naked eye. Because of the development of multi sensor imaging, RGB-IR image fusion can enhance the imaging quality in low light conditions. However, the existing fusion algorithms have insufficient enhancement, distorted detail and low contrast, which make it difficult to generate high-quality fusion results. In this paper, we propose a Transformer-CNN image fusion method which considers global-local features fusion for low light image enhancement. The ConvGRU module is developed to alternatively select the global and local features with Transformer and CNN network. To effectively improve the network performance, a learnable weight adaptive loss function is proposed to adjust the weight of loss functions during training. Numerous experiments prove that our method can enrich fusion image information, improve image contrast and edge in low-light scenes compared to state of the art methods.

Summary Predicting the daily tourism flow of scenic spots is of great significance for improving the management quality and the tourist experience. Affected by complex factors, daily tourism flow data have strong nonlinear characteristics. In this article, a multilayer neural network S‐CNNLSTM is put forward to make accurate short‐term tourism flow prediction. First, to reduce the redundant information between the influencing factors, sparse principal component analysis is adopted to reduce the data dimension. Then the processed data is input into a deep neural network framework that combines the convolutional neural network (CNN) and long short‐term memory (LSTM) network. CNN extracts local trends, and LSTM is introduced to learn the inner law of time series and make prediction. Finally, through the experiments with real data and the comparison algorithms, the stability and practicability of the proposed method are verified.