Hardware Implementation for Real-Time Haze Removal

Computer vision applications in outdoor environment are mainly affected by factors such as pollution, clouds, shadow, haze, fog. Herein, an algorithm for real-time haze detection and removal in image has been implemented. For this purpose, the Dark Channel Prior technique is used which is efficient method for haze removal and also contains information about the level of haze in the image. Initially, visibility index of image frame is estimated to determine whether image is hazy or not using haze detection model. Thereafter, hazy images are processed with haze removal model to enhance the image visibility. The haze removal model uses guided filter to accelerate the process. Non-hazy images do not require to process with haze removal model. The algorithm has been tested for four datasets, i.e., non-hazy, slightly hazy, medium hazy, and heavily hazy. The proposed algorithm is performing well and also able to tackle the halo effects at some extent.

Haze removal is useful in computational photography and computer vision applications. Single image haze removal is still a challenge for real-time embedded systems. In general, dark channel prior approach can effectively remove haze from a single image. However, the halo effect is the major problem of dehazing. In this study, adaptive filtering, which is easy for VLSI design, is introduced to refine the transmission map and improve the result of haze removal. A hardware architecture for our haze removal method is proposed to achieve the real-time requirement. A specific hardware component is designed to compute lengths of the paths and adaptive structuring element (ASE). The hardware architecture for haze removal is implemented in 180 nm CMOS technology and occupies 5.46 mm 2 with 143 K bit on-chip memory. The design can operate at 177MHz and support for HD (1280×720) 192 frame/s. Experiment results show that the hardware implementation is suitable in embedded system for real-time HD applications.

Haze significantly degrades the visibility for ship navigation and traffic monitoring in China's inland waterways. In this study, the authors propose a novel haze-removal method based on sky segmentation and dark channel prior to restore images. Sky segmentation is accomplished by using robust image matting and region growth algorithms. Then, the average image intensity of the sky region is chosen as the atmospheric light value to address the defect of dark channel prior. Experimental results show that their method can restore inland waterway images effectively; restored images are more natural and smoother than those obtained by the state-of-the-art haze removal algorithms.

A constrained total variation model for single image dehazing

Haze removal is important to improve performance of outdoor vision systems. However, it is challenging to remove haze from a single nighttime haze image. In this paper, a novel superpixel-based single image haze removal algorithm is proposed for nighttime haze images. The input nighttime image is first decomposed into a glow image and a glow-free nighttime haze image using their relative smoothness. A superpixel-based method is then introduced to compute the value of the atmospheric light and dark channel for each pixel in the glow-free haze image. The transmission map is decomposed from the dark channel of the glow-free haze image by the weighted guided image filter. Since superpixels usually adhere to the boundaries of objects well, a smaller local window size can be selected. As such, details in areas of fine structures are preserved better. In addition, to avoid noticeable noise in the sky area, an adaptive threshold is added to the transmission map when the nighttime haze image is restored. Experiments show that our method produces better results than the existing haze removal algorithms for nighttime haze images.

Outdoor scenes are usually affected by air particles and water vapor in the atmosphere. Haze is such phenomena happened due to atmospheric absorption and scattering. Haze affects the visibility and causes a degradation in the images quality of outdoor scenes, which in turn affects various image processing applications. In this paper, a review of the recent haze removal methods and its applications is introduced. The haze removal methods are categorized according to the input of their algorithm into two categories: multiple images and single image. Afterward, the different strategies in each category of the haze removal techniques are discussed and their characteristics are illustrated. Eventually, the quantitative parameters that evaluate the quality of the haze removed image are presented. This review paper highlights the importance of the haze removal methods in restoring a high-quality image relative to the quality of the original haze-free image. This haze removed image is a vital key in many computer vision applications including the visual navigation and object recognition and target detection.

The visibility and analyzability of MRI and CT images have a great impact on the diagnosis of medical diseases. Therefore, for low-quality MRI and CT images, it is necessary to effectively improve the contrast while suppressing the noise. In this paper, we propose an enhancement and denoising strategy for low-quality medical images based on the sequence decomposition Retinex model and the inverse haze removal approach. To be specific, we first estimate the smoothed illumination and de-noised reflectance in a successive sequence. Then, we apply a color inversion from 0-255 to the estimated illumination, and introduce a haze removal approach based on the dark channel prior to adjust the inverted illumination. Finally, the enhanced image is generated by combining the adjusted illumination and the de-noised reflectance. As a result, improved visibility is obtained from the processed images and inefficient or excessive enhancement is avoided. To verify the reliability of the proposed method, we perform qualitative and quantitative evaluation on five MRI datasets and one CT dataset. Experimental results demonstrate that the proposed method strikes a splendid balance between enhancement and denoising, providing performance superior to that of several state-of-the-art methods.

The visibility of outdoor images is usually significantly degraded by haze. Existing dehazing algorithms, such as dark channel prior (DCP) and colour attenuation prior (CAP), have made great progress and are highly effective. However, they all suffer from the problems of dark distortion and detailed information loss. This paper proposes an improved algorithm for single‐image haze removal based on dark channel prior with weighted guided coefficient and union self‐adaptive image enhancement. First, a weighted guided coefficient method with sampling based on guided image filtering is proposed to refine the transmission map efficiently. Second, the k ‐means clustering method is adopted to calibrate the original image into bright and non‐bright colour areas and form a transmission constraint matrix. The constraint matrix is then marked by connected‐component labelling, and small bright regions are eliminated to form an atmospheric light constraint matrix, which can suppress the halo effect and optimize the atmospheric light. Finally, an adaptive linear contrast enhancement algorithm with a union score is proposed to optimize restored images. Experimental results demonstrate that the proposed algorithm can overcome the problems of image distortion and detailed information loss and is more efficient than conventional dehazing algorithms.

As a challenging problem, image haze removal plays an important role in computer vision applications. The dark channel prior has been widely studied for haze removal since it is simple and effective; however, it still suffers from over‐saturation, artefacts and dark‐look. To resolve these problems, this study proposes a method of single image haze removal using content‐adaptive dark channel and post enhancement. The main contributions of this work are as follows: first, an associative filter, which can transfer the structures of a reference image and the grey levels of a coarse image to the filtering output, is employed to compute the dark channel efficiently and effectively. Secondly, the dark channel confidence is utilised to restrict the dark channel based on the content of the image. Finally, a post enhancement method is devised to map the luminance of the restored haze‐free image with the preservation of local contrast. Experimental results demonstrate that the proposed method significantly improves the visibility of the hazy image.

This paper proposes a new haze-removal scheme for automotive applications. In contrast that the previous works focus on enhancing the quality of haze-free images, the proposed algorithm sharpens the edges of lanes by adopting the weighting filter. Moreover, the previous internal steps, which cannot improve the sharpness of lanes, are removed to reduce the computing overheads. As a result, the proposed haze-removal algorithm improves the sharpness of lanes remarkably, while providing the real-time image processing for advanced driver assistance systems.

Haze significantly impacts vision reliability in various image processing and computer vision applications. Most existing dehazing techniques fail to meet computer vision requirements when handling severe fog and environmental haze. The key contributions of this study are as follows: we developed a novel approach that integrates image structure with second-generation wavelets using a convolutional neural network. Second, we applied the SGWs technique to incorporate the internal structure constraints of digital images, enhancing both overall structure and fine details in the dehazed images. Lastly, we propose a deep CNN to improve the performance of the developed model and to focus on the generation of detail textures rather than distinguishing the image degradation. We evaluated the performance of the proposed method with best haze removal algorithms using quantitative metrics such as PSNR and structural similarity index. In terms of computational efficiency, our method achieved significantly lower running times compared to learning-based techniques.

Haze removal or dehazing has been a challenge in the field of image restoration. Recently, He et al.. proposed a single image dehazing scheme based on an interesting statistical prior called the dark channel prior (DCP). By the DCP, two parameters in the haze image model, the atmospheric light and the transmission map, can be estimated easily. Consequently, the DCP scheme has attracted much attention in this field. Note that the DCP scheme relies on the block-based dark channel which is considered as a strong DCP assumption. In this paper, a pixel-based dark channel is introduced through which the atmospheric light and the transmission map are estimated. The pixel-based dark channel is considered as a weak DCP (WDCP) since its statistical property is not as strong as that in the block-based dark channel. With a similar manner in the DCP scheme, the atmospheric light is estimated through the pixel-based dark channel. To make the pixel-based dark channel feasible in the transmission map estimation, an adaptive scaling factor for the initial transmission map is employed and the pixel-based dark channel is applied as the guide image in the transmission map refinement by the guided image filtering. Furthermore, an objective assessment is used to evaluate the proposed WDCP scheme and the compared DCP scheme. Simulation results indicate that the proposed WDCP scheme is more efficient, 24.30 times faster than the DCP scheme on average. Moreover, the proposed WDCP scheme is of better subjective visual quality than the DCP scheme and the employed objective assessment generally agrees with the results in the given examples.

Removing haze or rain is one of the difficult problems in computer vision applications. On real-world road images, haze and rain often occur together, but traditional methods cannot solve this imaging problem. To address rain and haze problems simultaneously, we present a robust network-based framework consisting of three steps: image decomposition using guided filters, a frequency-based haze and rain removal network (FHRR-Net), and image restoration based on an atmospheric scattering model using predicted transmission maps and predicted rain-removed images. We demonstrate FHRR-Net’s capabilities with synthesized and real-world road images. Experimental results show that our trained framework has superior performance on synthesized and real-world road test images compared with state-of-the-art methods. We use PSNR (peak signal-to-noise) and SSIM (structural similarity index) indicators to evaluate our model quantitatively, showing that our methods have the highest PSNR and SSIM values. Furthermore, we demonstrate through experiments that our method is useful in real-world vision applications.

Haze usually limits the visibility and reduces the contrast of outdoor images. The removal of haze in a single image has always been a challenging problem. Recently, many methods have been proposed to effectively remove haze in harsh conditions. However, these methods fail to balance the dehazing effect and time consumption. This paper proposes a method based on HSV colour space to restore the visibility of uniform scattering medium. It can prevent the atmospheric light and transmission from being miscalculated. This method uses the brightness component of haze image to estimate the global atmospheric light, which reduces the influence of luminescent objects on atmospheric light estimation. Then, this paper deduces the estimation model of the saturation of scene radiance based on the atmospheric scattering model. In the meantime, according to the advantages and problems of the model, the fast estimation of the transmission is realized by using the stretching function. Finally, this paper solves the parameters in the model by iterative method. Since this method can estimate different media transmission for each pixel, better results can be obtained. The simulation results show that the algorithm is superior to the existing algorithms in terms of dehazing effect and time consumption.

Now that we have seen how images are formed through the interaction of 3D scene elements, lighting, and camera optics and sensors, let us look at the first stage in most computer vision applications, namely the use of image processing to preprocess the image and convert it into a form suitable for further analysis. Examples of such operations include exposure correction and color balancing, the reduction of image noise, increasing sharpness, or straightening the image by rotating it (Figure 3.1). While some may consider image processing to be outside the purview of computer vision, most computer vision applications, such as computational photography and even recognition, require care in designing the image processing stages in order to achieve acceptable results.KeywordsOriginal ImageDiscrete Cosine TransformHistogram EqualizationImage DenoisingImage PyramidThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.