Underwater images regularly exhibit color deviation and low contrast attributed to attenuation and scattering associated with wavelength and distance. For solving these two degradation problems, we propose an efficient underwater image enhancement model which takes a multi-scale fusion strategy as a pivot to concurrently achieve color balance and contrast enhancement. Specifically, we target two core degradation problems through two pre-processing steps. On the one hand, the white balance strategy enhances the appearance of the image by suppressing unnecessary colors to solve the color deviation problem. On the other hand, we employ a color-preserving adaptive histogram equalization (CP-AHE) to obtain an image with higher contrast by jointly processing the three color channels. In the fusion phase, a fusion strategy based on the detail-preserving decomposition of the edge-protected structural blocks is used to converge the white balance and CP-AHE inputs. Extensive experiments on common underwater image datasets have shown the advantages of the proposed method over other state-of-the-art underwater image methods in terms of visual and quantitative evaluation.

The quaternion wavelet transform is a new multiscale analysis tool. Firstly, this paper studies the standard orthogonal basis of scale space and wavelet space of quaternion wavelet transform in spatial L²(R²), proves and presents quaternion wavelet’s scale basis function and wavelet basis function concepts in spatial scale space L²(R²; H), and studies quaternion wavelet transform structure. Finally, the quaternion wavelet transform is applied to image denoising, and generalized Gauss distribution is used to model QWT coefficients’ magnitude distribution, under the Bayesian theory framework, to recover the original coefficients from the noisy wavelet coefficients, and so as to achieve the aim of denoising. Experimental results show that our method is not only better than many of the current denoising methods in the peak signal to noise ratio (PSNR), but also obtained better visual effect.

The properties of light propagation underwater typically cause color distortion and reduced contrast in underwater images. In addition, complex underwater lighting conditions can result in issues such as non-uniform illumination, spotting, and noise. To address these challenges, we propose an innovative underwater-image enhancement (UIE) approach based on maximum information-channel compensation and edge-preserving filtering techniques. Specifically, we first develop a channel information transmission strategy grounded in maximum information preservation principles, utilizing the maximum information channel to improve the color fidelity of the input image. Next, we locally enhance the color-corrected image using guided filtering and generate a series of globally contrast-enhanced images by applying gamma transformations with varying parameter values. In the final stage, the enhanced image sequence is decomposed into low-frequency (LF) and high-frequency (HF) components via side-window filtering. For the HF component, a weight map is constructed by calculating the difference between the current exposedness and the optimum exposure. For the LF component, we derive a comprehensive feature map by integrating the brightness map, saturation map, and saliency map, thereby accurately assessing the quality of degraded regions in a manner that aligns with the symmetry principle inherent in human vision. Ultimately, we combine the LF and HF components through a weighted summation process, resulting in a high-quality underwater image. Experimental results demonstrate that our method effectively achieves both color restoration and contrast enhancement, outperforming several State-of-the-Art UIE techniques across multiple datasets.

Medical image fusion is a crucial technology for biomedical diagnoses. However, current fusion methods struggle to balance algorithm design, visual effects, and computational efficiency. To address these challenges, we introduce a novel medical image fusion method based on the multi-scale shearing rolling weighted guided image filter (MSRWGIF). Inspired by the rolling guided filter, we construct the rolling weighted guided image filter (RWGIF) based on the weighted guided image filter. This filter offers progressive smoothing filtering of the image, generating smooth and detailed images. Then, we construct a novel image decomposition tool, MSRWGIF, by replacing non-subsampled shearlet transform's non-sampling pyramid filter with RWGIF to extract richer detailed information. In the first step of our method, we decompose the original images under MSRWGIF to obtain low-frequency subbands (LFS) and high-frequency subbands (HFS). Since LFS contain a large amount of energy-based information, we propose an improved local energy maximum (ILGM) fusion strategy. Meanwhile, HFS employ a fast and efficient parametric adaptive pulse coupled-neural network (AP-PCNN) model to combine more detailed information. Finally, the inverse MSRWGIF is utilized to generate the final fused image from fused LFS and HFS. To test the proposed method, we select multiple medical image sets for experimental simulation and confirm its advantages by combining seven high-quality representative metrics. The simplicity and efficiency of the method are compared with 11 classical fusion methods, illustrating significant improvements in the subjective and objective performance, especially for color medical image fusion.

The objective of image fusion is to synthesize information from multiple source images into a single, high-quality composite that is information-rich, thereby enhancing both human visual interpretation and machine perception capabilities. This process also establishes a robust foundation for downstream image-related tasks. Nevertheless, current deep learning-based networks frequently neglect the distinctive features inherent in source images, presenting challenges in effectively balancing the interplay between basic and detailed features. To tackle this limitation, we introduce a progressive decomposition network that integrates Lite Transformer (LT) and ResNet architecture for infrared and visible image fusion (IVIF). Our methodology unfolds in three principal stages: Initially, a foundational convolutional neural network (CNN) is deployed to extract coarse-scale features from the source images. Subsequently, the LT is employed to bifurcate these coarse features into basic and detailed feature components. In the second phase, to augment the detail information across various inter-layer extractions, we substitute the conventional ResNet preprocessing with a combination of coarse and LT module. Cascade LT operations are implemented following the initial two ResNet blocks (ResB), enabling two-branch feature extraction from these reconfigured blocks. The final stage involves the design of specialized fusion sub-networks to process the basic and detail information blocks extracted from different layers. These processed image feature blocks are then channeled through semantic injection module (SIM) and Transformer decoders to generate the fused image. Complementing this architecture, we have developed a semantic information extraction module that aligns with the progressive inter-layer detail extraction framework. The LT module is strategically embedded within the ResNet network architecture to optimize the extraction of both basic and detailed features across diverse layers. Moreover, we introduce a novel correlation loss function that operates on the basic and detail information between layers, facilitating the correlation of basic features while maintaining the independence of detail features across layers. Through comprehensive qualitative and quantitative analyses conducted on multiple infrared-visible datasets, we demonstrate the superior potential of our proposed network for advanced visual tasks. Our network exhibits remarkable performance in detail extraction, significantly outperforming existing deep learning methodologies in this domain.