Image Dehazing with Hybrid λ2-λ0 Penalty Mode

Due to the presence of turbid media, such as microdust and water vapor in the environment, outdoor pictures taken under hazy weather circumstances are typically degraded. To enhance the quality of such images, this work proposes a new hybrid λ2-λ0 penalty model for image dehazing. This model performs a weighted fusion of two distinct transmission maps, generated by imposing λ2 and λ0 norm penalties on the approximate regression coefficients of the transmission map. This approach effectively balances the sparsity and smoothness associated with the λ0 and λ2 norms, thereby optimizing the transmittance map. Specifically, when the λ2 norm is penalized in the model, an updated guided image is obtained after implementing λ0 penalty. The resulting optimization problem is effectively solved using the least square method and the alternating direction algorithm. The dehazing framework combines the advantages of λ2 and λ0 norms, enhancing sparse and smoothness, resulting in higher quality images with clearer details and preserved edges.

Visibility Enhancement of Scene Images Degraded by Foggy Weather Condition: An Application to Video Surveillance

:In recent years,video surveillance application played a significant role in our daily lives.Images taken during foggy and haze weather conditions for video surveillance application lose their authenticity and hence reduces the visibility.The reason behind visibility enhancement of foggy and haze images is to help numerous computer and machine vision applications such as satellite imagery,object detection,target killing,and surveillance.To remove fog and enhance visibility,a number of visibility enhancement algorithms and methods have been proposed in the past.However,these techniques suffer from several limitations that place strong obstacles to the real world outdoor computer vision applications.The existing techniques do not perform well when images contain heavy fog,large white region and strong atmospheric light.This research work proposed a new framework to defog and dehaze the image in order to enhance the visibility of foggy and haze images.The proposed framework is based on a Conditional generative adversarial network(CGAN)with two networks;generator and discriminator,each having distinct properties.The generator network generates fog-free images from foggy images and discriminator network distinguishes between the restored image and the original fog-free image.Experiments are conducted on FRIDA dataset and haze images.To assess the performance of the proposed method on fog dataset,we use PSNR and SSIM,and for Haze dataset use e,r−,andσas performance metrics.Experimental results shows that the proposed method achieved higher values of PSNR and SSIM which is 18.23,0.823 and lower values produced by the compared method which are 13.94,0.791 and so on.Experimental results demonstrated that the proposed framework Has removed fog and enhanced the visibility of foggy and hazy images.

Image Defogging Algorithm Based on Sky Region Segmentation and Dark Channel Prior

Based on image segmentation and the dark channel prior,this paper proposes a fog removal algorithm in the HSI color space.Usually,the dark channel prior based defogging methods easily produce color distortion and halo effect when applied on images with a large sky area,because the sky region does not meet the prior assumption.For this reason,our method presents a new threshold sky region segmentation algorithm using the initial transmission map of the intensity component I.Based on the segmentation result,the initial transmission map is modified in turn,and finally refined by the guided filter.The saturation components S is reconstructed using the low frequencies of the V-transform to reduce noise,and stretched by multiplying a constant related to the initial transmission map.Experimental results show that the proposed algorithm has low time complexity and compelling fog removal result in both visual effect and quantitative measurement.