Efficient Haze Removal Research Articles

Efficient Haze Removal from a Single Image Using a DCP-Based Lightweight U-Net Neural Network Model.

In this paper, we propose a lightweight U-net architecture neural network model based on Dark Channel Prior (DCP) for efficient haze (fog) removal with a single input. The existing DCP requires high computational complexity in its operation. These computations are challenging to accelerate, and the problem is exacerbated when dealing with high-resolution images (videos), making it very difficult to apply to general-purpose applications. Our proposed model addresses this issue by employing a two-stage neural network structure, replacing the computationally complex operations of the conventional DCP with easily accelerated convolution operations to achieve high-quality fog removal. Furthermore, our proposed model is designed with an intuitive structure using a relatively small number of parameters (2M), utilizing resources efficiently. These features demonstrate the effectiveness and efficiency of the proposed model for fog removal. The experimental results show that the proposed neural network model achieves an average Peak Signal-to-Noise Ratio (PSNR) of 26.65 dB and a Structural Similarity Index Measure (SSIM) of 0.88, indicating an improvement in the average PSNR of 11.5 dB and in SSIM of 0.22 compared to the conventional DCP. This shows that the proposed neural network achieves comparable results to CNN-based neural networks that have achieved SOTA-class performance, despite its intuitive structure with a relatively small number of parameters.

A 4K-Capable FPGA Implementation of Single Image Haze Removal Using Hazy Particle Maps

This paper presents a fast and compact hardware implementation using an efficient haze removal algorithm. The algorithm employs a modified hybrid median filter to estimate the hazy particle map, which is subsequently subtracted from the hazy image to recover the haze-free image. Adaptive tone remapping is also used to improve the narrow dynamic range due to haze removal. The computation error of the proposed hardware architecture is minimized compared with the floating-point algorithm. To ensure real-time hardware operation, the proposed architecture utilizes the modified hybrid median filter using the well-known Batcher’s parallel sort. Hardware verification confirmed that high-resolution video standards were processed in real time for haze removal.

An improvement of visual perception of single image by fusion of DCP with patch processing

Scattering of atmospheric particles in hazy or foggy weather conditions is the primary reason for the degradation of images. Though a level of saturation has been obtained in the improvement of restoration model to overcome the visual vividness those termed as a dehazing method, there still remains a void while restoring of the digital image. We, in this paper, have proposed a novel and efficient haze removal algorithm by considering an efficient prior knowledge like DCP in order to eliminate haze and, restore albedo by combined with diffusion patch process after soft mating to enhance again for better clarity and accuracy of a hazy input image. In this research analysis, we critically examined popular Dark Channel Prior haze resisting removals algorithm such as Fattal, He, Tan and Tarel with our proposed novel haze removal model, The image quality assessment such as qualitative and quantitative index of the de-hazed image is concerned with estimating the parameters such as RSME, PSNR, SSIM, FSIM, and FSIMc are also reported for measuring precise accuracy of our proposed methodology as compared to the existing one. This novel method found to be distinctive and ensures efficient production of a high-quality haze-free image.

VLSI Implementation for an Adaptive Haze Removal Method

In the real-time outdoor application of computer vision-based systems, haze removal, a preprocessing technique that can recover clear images from foggy ones, is necessary for object detection. Therefore, in this paper, an efficient haze removal method suitable for hardware design is proposed to obtain high quality fog-free images. Based on the atmosphere scattering model and the dark channel prior method, the atmospheric light of the whole image and the transmission map could be extracted. These are important and necessary parameters of the recovery model. Instead of using single global atmospheric light to restore foggy image, a local atmospheric light estimation method is applied in the proposed design to achieve optimal results. To ensure that the overall image is consistent without block artifacts, dynamic adjustment of local atmospheric light is made based on global atmospheric light. Additionally, to obtain a transmission map, a refined estimation method is performed to ease the halo effect. In terms of both quantitative and qualitative evaluations, the simulation results indicate that the proposed design exhibits superior performance without color oversaturation and distortion. To meet the requirements of real-time applications, a six-stage very-large-scale integration (VLSI) architecture for the proposed algorithm is implemented by using TSMC 0.13-um technology. The synthesis results show that the design yields a processing rate of approximately 200 Mpixels/s, which is rapid enough to facilitate Full HD resolution at 30 fps in real time.

Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features

This paper presents a computationally efficient haze removal and image enhancement methods. The major contribution of the proposed research is two-fold: (i) an accurate atmospheric light estimation using principal component analysis, and (ii) learning-based transmission estimation. To reduce the computational cost, we impose a constraint on the candidate pixels to estimate the haze components in the sub-image. In addition, the proposed method extracts modified haze-relevant features to estimate an accurate transmission using random forest. Experimental results show that the proposed method can provide high-quality results with a significantly reduced computational load compared with existing methods. In addition, we demonstrate that the proposed method can significantly enhance the contrast of low-light images according to the assumption on the visual similarity between the inverted low-light and haze images.

Hardware Implementation of a Fast and Efficient Haze Removal Method

In this letter, a fast and efficient haze removal method is presented. We employ an extremum approximate method to extract the atmospheric light and propose a contour preserving estimation to obtain the transmission by using edge-preserving and mean filters alternately. Our method can efficiently avoid the halo artifact generated in the recovered image. To meet the requirement of real-time applications, an 11-stage pipelined hardware architecture for our haze removal method is presented. It can achieve 200 MHz with 12.8 K gate counts by using TSMC 0.13- μm technology. Simulation results indicate that our design can obtain comparable results with the least execution time compared to previous algorithms and is suitable for low-cost high-performance hardware implementation for haze removal.