Multi-Scale Fusion of Enhanced Hazy Images Using Particle Swarm Optimization and Fuzzy Intensification Operators

Dehazing from a single image is still a challenging task, where the thickness of the haze depends on depth information. Researchers focus on this area by eliminating haze from the single image by using restoration techniques based on haze image model. Using haze image model, the haze is eliminated by estimating atmospheric light, transmission, and depth. A few researchers have focused on enhancement based methods for eliminating haze from images. Enhancement based dehazing algorithms will lead to saturation of pixels in the enhanced image. This is due to assigning fixed values to the parameters used to enhance an image. Therefore, the enhancement based methods fail in the proper tuning of the parameters. This can be overcome by optimizing the parameters that are used to enhance the images. This paper describes the research work carried to derive two enhanced images from a single input hazy image using particle swarm optimization and fuzzy intensification operators. The two derived images are further fused using multi-scale fusion technique. The objective evaluation shows that the entropy of the haze eliminated images is comparatively better than the state-of-the-art algorithms. Also, the fog density is measured using an evaluator known as fog aware density evaluator (FADE), which considers all the statistical parameters to differentiate a hazy image from a highly visible natural image. Using this evaluator we found that the density of the fog is less in our proposed method when compared with enhancement based algorithms used to eliminate haze from images

Exploring Dehazing Methods For Remote Sensing Imagery: A Review

Remote sensing imagery plays a pivotal role in numerous applications, from environmental monitoring to disaster management. However, the occurrence of haze which is atmospheric often reduces the quality and interpretability of these images.  Atmospheric Haze reduces visibility of remote sensed images by reducing contrast and causing colour distortions.  Dehazing techniques are employed to improve the perceptibility and clarity affected images by haze. In this review, we delve into the realm of dehazing methods specifically tailored for remote sensing imagery, aiming to shed light on their efficacy and applicability. We focus on a comprehensive comparison of four prominent dehazing techniques: Histogram Equalization (HE), Light Channel Prior (LCP), Contrast Enhancement Filters (CEF), and Dark Channel Prior (DCP). These methods, representing a spectrum of approaches, are evaluated based on key quality metrics of images, including PSNR, MSE and SSIM

Estimation of Parameters in Atmospheric Scattering Dehazing Model in Accordance with Visual Characteristics

In view of the problem that the restoration effect of the daytime defogging algorithm is not ideal, especially the over-enhancement and color distortion in the sky and its nearby. A new parameter estimation method for atmospheric scattering model is proposed. Firstly, the sky and non-sky areas are segmented. Then, estimating the atmospheric light at the junction, and then corresponding restrictions on the value of transmittance according to the change of the depth of field. Finally, the transmittance is optimized by context-based regularization, so that the final image after dehazing is more in line with the visual characteristics. Through the subjective comparison and analysis with the existing mainstream algorithms, the dehazing effect of the proposed method has the advantages of low noise and high color recovery, especially in the sky. The restoration with the non-sky junction is the best, enriching the details that other algorithms have not restored and the colors are true and natural

Image contrast enhancement for preserving entropy and image visual features

Histogram equalization is essential for low-contrast enhancement in image processing. Several methods have been proposed; however, one of the most critical problems encountered by existing methods is their ability to preserve information in the enhanced image as the original. This research proposes an image enhancement method based on a histogram equalization approach that preserves the entropy and fine details similar to those of the original image. This is achieved through proposed probability density functions (PDFs) that preserve the small gray values of the usual PDF. The method consists of several steps. First, occurrences and clipped histograms are extracted according to the proposed thresholding. Then, they are equalized and used by a proposed transferring function to calculate the new pixel values in the enhanced image. The proposed method is compared with widely used methods such as Clahe, CS, HE, and GTSHE. Experiments using benchmark datasets and entropy, contrast, PSNR, and SSIM measurements are conducted to evaluate the performance. The results show that the proposed method is the only one that preserves the entropy of the enhanced image of the original image. In addition, it is efficient and reliable in enhancing image quality. This method preserves fine details and improves image quality, supporting computer vision and pattern recognition fields