Single image defogging via multi-exposure image fusion and detail enhancement

Outdoor cameras play an important role in monitoring security and social governance.As a common weather phenomenon,haze can easily affect the quality of camera shooting,resulting in loss and distortion of image details.This paper proposes an improved multi-exposure image fusion defogging technique based on the artificial multi-exposure image fusion(AMEF)algorithm.First,the foggy image is adaptively exposed,and the fused image is subsequently obtained via multiple exposures.The fusion weight is determined by the saturation,contrast,and brightness.Finally,the image fused by a multi-scale Laplacian algorithm is enhanced with simple adaptive details to obtain a clearer defogging image.It is subjectively and objectively verified that this algorithm can obtain more image details and distinct picture colors without a priori information,effectively improving the defogging ability.

Color image detail enhancement based on quaternion guided filter

Color image enhancement is an active research field in image processing. Currently, many image enhancement methods are capable of enhancing the details of the color image. However, these methods only process the red, green and blue （RGB） color channels separately, which leads to color distortion easily. In order to overcome this problem, the paper presents an approach to integrate the quaternion theory into the traditional guided filter to obtain a quaternion guided filter （QGF）. This method makes full use of the color information of an image to realize the holistic processing of RGB color channels. So as to preserve color information while enhancing details, this paper proposes a color image detail enhancement algorithm based on the QGF. Experimental results show that the proposed algorithm is effective in the applications of the color image detail enhancement, and enables image＇s edges to be more prominent and texture clearer while avoiding color distortion. Compared with the existing image enhancement methods, the proposed method achieves better enhancement performance in terms of the visual quality and the objective evaluating indicators.

AIDEDNet:anti-interference and detail enhancement dehazing network for real-world scenes

The haze phenomenon seriously interferes the image acquisition and reduces image quality.Due to many uncertain factors,dehazing is typically a challenge in image processing.The most existing deep learning-based dehazing approaches apply the atmospheric scattering model(ASM)or a similar physical model,which originally comes from traditional dehazing methods.However,the data set trained in deep learning does not match well this model for three reasons.Firstly,the atmospheric illumination in ASM is obtained from prior experience,which is not accurate for dehazing real-scene.Secondly,it is difficult to get the depth of outdoor scenes for ASM.Thirdly,the haze is a complex natural phenomenon,and it is difficult to find an accurate physical model and related parameters to describe this phenomenon.In this paper,we propose a black box method,in which the haze is considered an image quality problem without using any physical model such as ASM.Analytically,we propose a novel dehazing equation to combine two mechanisms:interference item and detail enhancement item.The interference item estimates the haze information for dehazing the image,and then the detail enhancement item can repair and enhance the details of the dehazed image.Based on the new equation,we design an antiinterference and detail enhancement dehazing network(AIDEDNet),which is dramatically different from existing dehazing networks in that our network is fed into the haze-free images for training.Specifically,we propose a new way to construct a haze patch on the flight of network training.The patch is randomly selected from the input images and the thickness of haze is also randomly set.Numerous experiment results show that AIDEDNet outperforms the state-of-the-art methods on both synthetic haze scenes and real-world haze scenes.

Two-Layer Attention Feature Pyramid Network for Small Object Detection

Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.

A Small Target Detection Method for Sea Surface Based on Guided Filtering and Local Mean Gray Difference

The traditional small target detection algorithm often results in a high false alarm rate on the sea surface background. To address this issue, a small target detection method based on guided filtering and local average gray level difference was proposed in this paper for the sea surface. Firstly, the method enhanced the details of the small targets by employing guided filtering to suppress the background clutter and noise in the sea surface image. Subsequently, the local average gray level difference of each point in the image was calculated to further distinguish the targets from other interference points. Finally, the threshold segmentation method was utilized to obtain the actual small targets on the sea surface. After conducting experiments on various sea surface scenes, the LSCRG, BSF, and ROC curve were computed for the proposed method and five other algorithms. Comparative analysis with BS, Top-hat, TDLMS, Max-median, and LCM demonstrates the superiority of the proposed method for infrared small target detection on the sea surface.

Image enhancement for crop trait information acquisition system

Collecting images using portable devices is an effective and convenient method for acquiring crop trait information.Because of uncertain environmental conditions in the field,enhancement is necessary to improve the visual quality of images.With this aim,here we propose an adaptive image enhancement method based on guided filtering.Our method automatically calculates the enhancement weights of the detail in an image according to the distribution characteristics of the illumination intensity of a crop image,so as to adaptively adjust the contrast of the image.To verify the effectiveness of the proposed algorithm,we performed enhancement experiments on 50 images of four kinds of cucumber leaf tissues,namely,leaves infected with target spot,powdery mildew,and downy mildew,and healthy leaves.The results showed that our proposed method substantially improved the visual quality of the images.Moreover,the mean ratios of the contrast to color difference obtained using the proposed method were higher than the mean ratios obtained using five conventional enhancement methods.We consider the proposed method for image enhancement will be a valuable addition to the crop trait information acquisition system(http://ebreed.com.cn/).

Physically-based enhancement in shallow water resimulation

To resimulate a customized fluid derived product by analyzing an existing fluid is significant and difficult.This paper proposes a driven model recovery method,which is challenging in fluid resimulation customization.First,fluid physical properties are calculated under the constraints of appearance and dynamic behavior of the example water.Second,a hybrid particle lattice Boltzmann method for shallow water(LBMSW)is recovered from the dynamic geometry on fluid surface.As it is found that the resimulation details fade gradually with LBMSW auto-advection,a physically-based enhancement scheme is presented.A nonlinear algorithm is introduced to stretch the faded density to retain resimulation details.Experiments show that the proposed approach can obtain more realistic resimulation products in several challenging scenarios.