Existing Enhancement Algorithms Research Articles

Low-illumination image enhancement with logarithmic tone mapping

Abstract For low-illumination video sequences, some existing enhancement algorithms have some problems, such as image over-enhancement, color distortion, and inadequate detail processing. Based on luminance detection, we add logarithmic tone mapping to optimize the existing algorithms. The color space of low-illumination video image is converted from the red, green, blue mode to the hue-saturation-intensity mode, and then, logarithmic tone enhancement is applied to the image. Algorithm in this study has an obvious effect on image luminance enhancement and details processing, which makes the low-illumination video show a clear image with more natural visual effect, thus improving the quality of low-illumination video. This algorithm can avoid the problems of overexposure, color distortion, and blurring of detail processing under low illumination. The infrared spectrum of the object can be captured by infrared detection equipment, and the purpose of image enhancement can be achieved by applying the infrared spectrum of the object.

Infrared Sensation-Based Salient Targets Enhancement Methods in Low-Visibility Scenes.

Thermal imaging is an important technology in low-visibility environments, and due to the blurred edges and low contrast of infrared images, enhancement processing is of vital importance. However, to some extent, the existing enhancement algorithms based on pixel-level information ignore the salient feature of targets, the temperature which effectively separates the targets by their color. Therefore, based on the temperature and pixel features of infrared images, first, a threshold denoising model based on wavelet transformation with bilateral filtering (WTBF) was proposed. Second, our group proposed a salient components enhancement method based on a multi-scale retinex algorithm combined with frequency-tuned salient region extraction (MSRFT). Third, the image contrast and noise distribution were improved by using salient features of orientation, color, and illuminance of night or snow targets. Finally, the accuracy of the bounding box of enhanced images was tested by the pre-trained and improved object detector. The results show that the improved method can reach an accuracy of 90% of snow targets, and the average precision of car and people categories improved in four low-visibility scenes, which demonstrates the high accuracy and adaptability of the proposed methods of great significance for target detection, trajectory tracking, and danger warning of automobile driving.

Smoke removal and image enhancement of laparoscopic images by an artificial multi-exposure image fusion method

In laparoscopic surgery, image quality is often degraded by surgical smoke or by side effects of the illumination system, such as reflections, specularities, and non-uniform illumination. The degraded images complicate the work of the surgeons and may lead to errors in image-guided surgery. Existing enhancement algorithms mainly focus on enhancing global image contrast, overlooking local contrast. Here, we propose a new Patch Adaptive Structure Decomposition utilizing the Multi-Exposure Fusion technique to enhance the local contrast of laparoscopic images for better visualization. The set of under-exposure level images is obtained from a single input blurred image by using gamma correction. Spatial linear saturation is applied to enhance image contrast and to adjust the image saturation. The Multi-Exposure Fusion (MEF) is used on a series of multi-exposure images to obtain a single clear and smoke-free fused image. MEF is applied by using adaptive structure decomposition on all image patches. Image entropy based on the texture energy is used to calculate image energy strength. The texture entropy energy determined the patch size that is useful in the decomposition of image structure. The proposed method effectively eliminate smoke and enhance the degraded laparoscopic images. The qualitative results showed that the visual quality of the resultant images is improved and smoke-free. Furthermore, the quantitative scores computed of the metrics: FADE, Blur, JNBM, and Edge Intensity are significantly improved as compared to other existing methods.

Adaptive Dark Channel Prior Enhancement Algorithm for Different Source Night Vision Halation Images

The existing enhancement algorithms amplify the halation area and noise when enhancing the night vision halation image. Therefore, this paper proposes an adaptive dark channel prior (ADCP) enhancement algorithm for the different source night vision halation image. The algorithm constructs an adaptive transmittance function according to the relationship between the initial transmittance and the critical gray value of halation. The function can automatically adjust the transmittance according to the halation degree in the night vision image, which ensure the ADCP algorithm to achieve the adaptive enhancement of the images. The experimental results show that the proposed algorithm can effectively improve the clarity and contrast of visible and infrared images in night vision, and avoid over-enhancement of the halation region of visible images. When the proposed algorithm is applied to the anti-halation processing of different source night vision image fusion, the halation elimination of the fused image is more complete, the details of the dark area such as edge, brightness and color are moderately improved, and the overall visual effect is better than the existing enhancement algorithms. The effectiveness and universality of the proposed algorithm are verified for processing different night vision halation scene images.

Progressive Two-Stage Network for Low-Light Image Enhancement.

At night, visual quality is reduced due to insufficient illumination so that it is difficult to conduct high-level visual tasks effectively. Existing image enhancement methods only focus on brightness improvement, however, improving image quality in low-light environments still remains a challenging task. In order to overcome the limitations of existing enhancement algorithms with insufficient enhancement, a progressive two-stage image enhancement network is proposed in this paper. The low-light image enhancement problem is innovatively divided into two stages. The first stage of the network extracts the multi-scale features of the image through an encoder and decoder structure. The second stage of the network refines the results after enhancement to further improve output brightness. Experimental results and data analysis show that our method can achieve state-of-the-art performance on synthetic and real data sets, with both subjective and objective capability superior to other approaches.

An Image Enhancement Algorithm Based on Fractional-Order Phase Stretch Transform and Relative Total Variation.

The main purpose of image enhancement technology is to improve the quality of the image to better assist those activities of daily life that are widely dependent on it like healthcare, industries, education, and surveillance. Due to the influence of complex environments, there are risks of insufficient detail and low contrast in some images. Existing enhancement algorithms are prone to overexposure and improper detail processing. This paper attempts to improve the treatment effect of Phase Stretch Transform (PST) on the information of low and medium frequencies. For this purpose, an image enhancement algorithm on the basis of fractional-order PST and relative total variation (FOPSTRTV) is developed to address the task. In this algorithm, the noise in the original image is removed by low-pass filtering, the edges of images are extracted by fractional-order PST, and then the images are fused with extracted edges through RTV. Finally, extensive experiments were used to verify the effect of the proposed algorithm with different datasets.

Fast color balance and multi-path fusion for sandstorm image enhancement

Outdoor images captured during sandstorm weather condition frequently yield color cast and poor visibility, which causes some applications to fail in computer vision, such as video surveillance and object recognition systems. In this paper, a fast color balance method followed by an effective fusion model is proposed to enhance the sandstorm-degraded images. Firstly, color channel compensation and piece-wise affine transform balance the aberrant pixels obtained by camera and remove color cast. Then, multi-path fusion comprising of underexposure and contrast-enhanced inputs is used for visibility enhancement, where saturation and Laplacian contrast are measured as weight maps and constructed for Gaussian pyramids. In order to reduce block effects and artifacts of the reconstructed image, we introduce the multi-scale strategy for each path. Experimental results of both synthetic and real-world sandstorm images demonstrate that the proposed color balance method features high computational efficiency and performs much better than comparative methods in terms of image quality. In addition, our fusion-based method also outperforms existing enhancement algorithms of sandstorm image via qualitative and quantitative evaluations.

Ridge Orientation Estimation and Verification Algorithm for Fingerprint Enhancement

Fingerprint image enhancement is a common and critical step in fingerprint recognition systems. To enhance the images, most of the existing enhancement algorithms use filtering techniques that can be categorized into isotropic and anisotropic according to the filter kernel. Isotropic filtering can properly preserve features on the input images but can hardly improve the quality of the images. On the other hand, anisotropic filtering can effectively remove noise from the image but only when a reliable orientation is provided. In this paper, we propose a ridge orientation estimation and verification algorithm which can not only generate an orientation of ridge flows, but also verify its reliability. Experimental results show that, on average, over 51 percent of an image in the NIST-4 database has reliable orientations. Based on this algorithm, a hybrid fingerprint enhancement algorithm is developed which applies isotropic filtering on regions without reliable orientations and anisotropic filtering on regions with reliable orientations. Experimental results show the proposed algorithm can combine advantages of both isotropic and anisotropic filtering techniques and generally improve the quality of fingerprint images.