Multimedia image evaluation based on blockchain, visual communication design and color balance optimization

The relationship between image processing and image analysis is inseparable. With the increasing demand for multimedia visual images, the quality of image analysis is also increasing. However, in image processing and computer vision tasks, protecting users' privacy and preventing data leakage and abuse are not handled well. Image enhancement and nonlinear image color balance algorithm are applied to improve the visual quality of multimedia visual images and make them clearer and fuller. The article utilized image enhancement and a non-linear image color balance algorithm to improve the processing effect before visual image analysis. It also utilized the encryption mechanism of blockchain technology to detect the similarity of multimedia visual images. By comparing the feature points of the images, similar images were matched to address the copyright issue of the images. After experimental testing, the effect of image enhancement is significant, and the histogram of image equalization is significantly better than the original image. In the experiment of image analysis, the computer accurately classified visual images with different attributes. Finally, in the similarity detection algorithm of blockchain, the test results showed that when the number of image transactions reaches 500, the difference hash algorithm takes 1.13 s and 0.78 s to calculate the similarity comparison between the original and secondary images. The differential hash algorithm of blockchain is significantly superior to the Message-Digest Algorithm (MD5) in terms of computational speed and resource consumption. It has better image similarity detection performance and can also provide better image copyright protection mechanisms.

PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement sub-network (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://rmcong.github.io/proj_PUGAN.html.

Dual-path joint correction network for underwater image enhancement

Acquired underwater images often suffer from severe quality degradation, such as color shift and detail loss due to suspended particles' light absorption and scattering. In this paper, we propose a Dual-path Joint Correction Network (DJC-NET) to cope with the above degenerate issues, preserving different unique properties of underwater images in a dual-branch way. The design of the light absorption correction branch is to improve the selective absorption of light in water and remove color distortion, while the light scattering correction branch aims to improve the blur caused by scattering. Concretely, in the light absorption correction path, we design the triplet color feature extraction module, which balances the triplet color distribution of the degraded image through independent feature learning between R, G, and B channels. In the light scattering correction path, we develop a dual dimensional attention mechanism to extract the texture information from the features, aiming to recover sufficient details by more effective feature extraction. Furthermore, our method utilizes the multi-scale U-net to adaptively fusion features from different paths to generate enhanced images. Extensive visual and objective experimental results demonstrate that our method outperforms state-of-the-art methods in various underwater scenes.

Recovery for underwater image degradation with multi-stage progressive enhancement

Optical absorption and scattering result in quality degradation of underwater images, which hampers the performance of underwater vision tasks. In practice, a well-posed underwater image recovery requires a combination of scene specificity and adaptability. To this end, this paper breaks down the overall recovery process into in-situ enhancement and data-driven correction modules, and proposes a Multi-stage Underwater Image Enhancement (MUIE) method to cascade the modules. In the in-situ enhancement module, a channel compensation with scene-relevant supervision is designed to address different degrees of unbalanced attenuation, and then the duality-based computation inverts the result of running a enhancement on inverted intensities to recover the degraded textures. In response to different scenarios, a data-driven correction, encoding corrected color-constancy information under data supervision, is performed to correct the improper color appearance of in-situ enhanced results. Further, under the collaboration between scene and data information, the recovery of MUIE avoids ill-posed response and reduces the prior dependence of specific scenes, resulting in a robust performance in different underwater scenes. Recovery comparison results confirm that the recovery of MUIE shows the superiority of scene clarity, realistic color appearance and evaluation scores. With the recovery of MUIE, the Underwater Image Quality Measurement (UIQM) scores of recovery-challenging images in the UIEB dataset were improved from 1.59 to 3.92.