A study on Retinex based method for image enhancement

In this paper the image processing method is used to enhance the pneumonia bacteria images. This paper recognized the bacteria images based on two domains. The enhancement techniques used for bacteria image enhancement were median filter, wiener filter, single scale retinex and multiscale retinex. Image enhancement has a very important role in digital image processing. The median and wiener filters were used for grayscale image enhancement. Then single scale retinex and multiscale retinex were used for color image enhancement. Based on performance metrics identified median filter is suitable for bacteria images in grayscale image enhancement and multiscale retinex is suitable for bacteria color image enhancement (Tab. 2, Fig. 8, Ref. 21).

This paper proposes a novel approach to reduce the operation time in the Multi-scale Retinex (MSR) while preserving the result image quality. Existing MSR algorithms take much time because they have many arithmetic operations. The proposed algorithm uses MSR weights selection algorithm to determine the important Single-scale Retinex (SSR) among the small, middle, and large SSRs. To reduce the operation time, we reduce the MSR weights selection arithmetic operation but have similar weights. And then, we skip SSR operations to reduce the operation time except dominant SSR when one SSR weight is dominant. Finally, we compare the MSR result image with the original image to reduce graying out. In the experiment, the proposed algorithm has 25%, 13% and 204% performance increment compared with benchmark method1, 2 and 3 and similar result image quality compared with conventional methods.

Image contrast enhancement is an essential operation for various image processing applications, especially for segmentation and feature extraction. However, different contrast enhancement methods contain various performance on the same images. To automatically choose the suitable algorithm for image contrast enhancement, in this paper, we put forward a different Retinex based method according to image feature fusion. First, five features, including gray value, contrast, definition, edge and entropy, are defined to characterize various image. Then, five fusion rules are designed to distinguish images with low contrast, foggy, monotonous color, rich in detail or color. Finally, the single scale Retinex (SSR), the multi-scale Retinex (MSR) and MSR in HSV color space (MSR_HSV) are introduced to improve image contrast. Experiments on various images drawn from different data sets prove the effectiveness of our proposed method.

A contrast enhancement technique using scaling of internal noise of a dark image in discrete cosine transform (DCT) domain has been proposed in this paper. The mechanism of enhancement is attributed to noise-induced transition of DCT coefficients from a poor state to an enhanced state. This transition is effected by the internal noise present due to lack of sufficient illumination and can be modeled by a general bistable system exhibiting dynamic stochastic resonance. The proposed technique adopts a local adaptive processing and significantly enhances the image contrast and color information while ascertaining good perceptual quality. When compared with the existing enhancement techniques such as adaptive histogram equalization, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, multi-contrast enhancement, multi-contrast enhancement with dynamic range compression, color enhancement by scaling, edge-preserving multi-scale decomposition and automatic controls of popular imaging tool, the proposed technique gives remarkable performance in terms of relative contrast enhancement, colorfulness and visual quality of enhanced image.

Low light images suffer from a low dynamic range and severe noise due to low signal-to-noise ratio (SNR). In this paper, we propose joint contrast enhancement and noise reduction of low light images via just-noticeable-difference (JND) transform. We adopt the JND transform to achieve both contrast enhancement and noise reduction based on human visual perception. First, we generate a JND map based on an the human visual system (HVS) response model from foreground and background luminance, called JND transform. Second, for base image, we perform perceptual contrast enhancement based on luminance adaptation to effectively allocate a dynamic range to each gray level while preventing under enhancement (tone distortion) and over-enhancement. Third, we refine the JND map using Weber's law, luminance adaptation and visual masking. Weber's law enhances the JND map based on the luminance variation after contrast enhancement. Luminance adaptation suppresses noise for smooth regions, while visual masking enforces detail enhancement for textural regions. Fourth, we perform inverse JND transform to generate the enhanced luma channel from the JND map and base image. Finally, we conduct chroma denoising by transferring texture information of the enhanced luma channel to the chroma channels with guided filtering. Experimental results show that the proposed method achieves both contrast enhancement and noise reduction for low light images as well as outperforms state-of-the-art methods in terms of quantitative measurements.

Infrared imaging plays a crucial role in the detection of weak targets (e.g., low-altitude vehicles) against complex backgrounds. However, challenges such as low contrast, high noise and uneven illumination often degrade image quality and hinder reliable target detection. In this paper, In order to address these problems, we systematically investigate six image enhancement algorithms: grey-scale transformation, histogram equalisation (HE), contrast-constrained adaptive histogram equalisation (CLAHE), Laplace filtering, single-scale Retinex (SSR), and multi-scale Retinex with colour reduction (MSRCR). We use objective metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) to evaluate the enhancement performance. In addition, we refine the evaluation by analysing PSNR and SSIM for the entire image as well as for regions of interest (ROIs) containing weak targets. This regional evaluation allows us to gain a more targeted understanding of the impact of enhancement on the detection of critical regions. Experimental results show that the Retinex-based method with Raschmann filtering significantly improves target visibility while suppressing background noise. This work provides a solution for infrared image enhancement and evaluation that has practical implications for aerospace sensing systems.

We address the problem of contrast enhancement for color images. Methods directly derived from gray-level enhancement such as histogram equalization produce significant artifacts, including severe color shifts. Other enhancement techniques that are derived from the Retinex theory may suffer from strong `halo' effects. Our method to enhance images is inspired from the Retinex theory and tries to mimic human color perception. The method helps in achieving color constancy and also results in color contrast enhancement. We express the intensity as a product of illumination and reflectance and estimate these separately. Enhancement is then applied to the illuminant component only. Non-local means filter is used to estimate the illuminant and then the enhancement of the illumination is performed automatically without any manual intervention and multiplied back by the reflectance to obtain enhancement. We compare our results with those from other enhancement techniques and with results from commercial software packages such as PhotoFlair® that uses multi-scale retinex with color restoration (MSRCR) and Picasa¿ and observe that our results are consistently 'visually better'. Finally, we perform a statistical analysis of our results and quantitatively show that our approach produces effective and substantial image enhancement. This is validated by ratings from human observers.

Purpose. The aim of the article is to increase the efficiency of methods of spread of invasive plant species remote sensing to assess potential economic damage to the environment and agriculture through the use of intelligent surveillance systems. Methodology of research. The following methods were used in the study: monographic – for studying literature sources; machine learning method – for model training; three-way data split method for assessing model accuracy and statistical analysis for comparing recognition results; basic (traditional) methods for image enhancement; gradient image processing (Poisson mixing) – for image fusion. Findings. A model for improving the efficiency of methods for remote recognition of the spread of invasive plant species was developed and proposed. The performance of the proposed model was compared with the model based on traditional image augmentation methods. It was found that the CAIPIS method, which is the basis of the model, achieves significantly higher results in detecting invasive plant species, especially when the data set is limited. In addition, the method helps to increase the reliability and generalization of deep-learning models for detecting the spread of invasive plant species. The CAIPIS method provides means to create more diverse and realistic training samples, which helps to improve the generalization ability of the deep learning model in detecting invasive plants in aerial imagery of agricultural fields and ecosystems obtained using unmanned aerial vehicles. Originality. A new method for enhancing (augmenting) aerial images of crop fields is presented, which aims to improve the performance of invasive plant detection models by creating diverse and realistic training samples and increasing the variability of the training dataset to improve the performance of such models. Practical value. The application of the aerial images enhancement (augmentation) method will increase the effectiveness of controlling the spread of invasive pests in ecosystems. Key words: invasive species, phytoinvasion, augmentation, recognition, remote sensing.

Images are often significantly degraded by fog and their values are greatly reduced. According to retinex theory and the properties of fogged images, fog degradation can be eliminated by modifying illumination variation. The multi-scale retinex (MSR) algorithm was analyzed and an enhancement method directing to fogged images was proposed. Firstly, a preliminary global luminance was adjusted by linear stretching and screen algorithm to increase the luminance of darker pixels and compress the dynamic range as well. Then the local contrast was increased by an improved algorithm based on MSR. Finally, the output of local contrast enhancement was processed by the normal clipping stretching to realize color correction. Experiments show that the algorithm can effectively remove fog degradation from color images.

The purpose of this article is to describe effective methods in image enhancement and their application in processing gray scale as well as color images. Methods of image processing can be classified into two classes describing the image enhancement in the spatial domain (by gray‐scale transformations) and in the frequency domain. Examples of the first class are the well‐known methods of histogram equalization, single‐scale retinex, and multiscale retinex for grayscale and color images. The image enhancement in the frequency domain is mainly related to the application of discrete Fourier transform (DFT) with fast algorithms, although other transforms are also used. The methods of alpha‐rooting withDFTfor processing grayscale images and with quaternionDFTfor color images are described in this article, together with the method of retinex. The methods of image enhancement by the discrete signal‐induced heap transform are also presented. The quality of images is estimated by the known image enhancement measureEMEwith its generalizationEMECfor color images.

An magnified image enhancement algorithm is presented in this study. Image interpolation is an important image magnification tool, but the magnified image has not been changed by the traditional image interpolation methods even if the magnified image is not satisfaction. Basically, the interpolating surface is determined uniquely for the given interpolating data. Namely the interpolating surface (magnified image) is fixed when the interpolating data (gray value of original image) is given. If the magnified image needs to be enhanced, another enhancement method must be chosen after image magnifying. The image magnification and enhancement are separated. The image natural attribute will be effected. To overcome the disadvantages of the traditional methods, a new method which combined the image magnification and enhancement is proposed. A bivariate rational interpolation with parameters is used in the algorithm. The value of the interpolating function at any point in the interpolating region can be modified under the condition that the interpolating data are not changed by selecting the suitable parameters. Using the surface control, the enlarged image enhancement is implemented. The experiment shows that the algorithm is efficient.

We address the problem of the way in which different, wellknown and commonly used image enhancement techniques effect qualitative human image judgments, and quantitative target detection performance. Two different psychophysical experiments are specially designed and performed to show how smoothing, full dynamic range exploitation, contrast and edge enhancement affect human abilities regarding target detection in IR imagery, and how this effect varies with imaging time and target thermal condition. Analysis of results shows systematic dependence on enhancement methods, very good agreement between image quality judgments and both target detection rates and probabilities. Significant interaction between enhancement methods and the time of day when the image is taken is also found.

Security methods based on biometrics are gaining importance increasingly for the past few years due to several major security disasters that occurred in the last decade. The successful development of biometric security systems cannot only minimise such threats but may also help in preventing them from happening on a global scale. Biometric security methods take into account humans' unique physical or behavioural traits that help to identify them based on their intrinsic characteristics. Such characteristics are usually obscure due to low quality images produced by Surveillance camera. In this paper, we propose an image enhancement method based on contrast enhancement and colour balancing methods in order to improve the quality of the images. We have improved the colour balancing method significantly in order to get better performance. The proposed method has been applied to the enhancement of low quality images produced by surveillance cameras. The performance of the proposed method is evaluated by applying three face detection methods to surveillance images before and after enhancement using the proposed method. The results show a significant improvement in face detection when the proposed method was applied.

Low-light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low-light image enhancement method that can be combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model-based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low-light images as input and the enhanced V channel (V channel of the enhanced image) as a condition during testing, and then it can re-enhance the contrast and brightness of the low-light image and at the same time reduce noise and color distortion. In addition, it takes 23 ms to process a color image with the resolution 400*600 on a 1080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining it with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness when the contrast and brightness of the reference are not good.

As the dynamic range of a digital camera is narrower than that of a real scene, the captured image requires a tone curve or contrast correction to reproduce the information in dark regions. Yet, when using a global correction method, such as histogram-based methods and gamma correction, an unintended contrast enhancement in bright regions can result. Thus, a multiscale retinex algorithm using Gaussian filters was already proposed to enhance the local contrast of a captured image using the ratio between the intensities of an arbitrary pixel in the captured image and its surrounding pixels. The intensity of the surrounding pixels is estimated using Gaussian filters and weights for each filter, and to obtain better results, these Gaussian filters and weights are adjusted in relation to the captured image. Nonetheless, this adjustment is currently a subjective process, as no method has yet been developed for optimizing the Gaussian filters and weights according to the captured image. Therefore, this article proposes local contrast enhancement based on an adaptive multiscale retinex using a Gaussian filter set adapted to the input image. First, the weight of the largest Gaussian filter is determined using the local contrast ratio from the intensity distribution of the input image. The other Gaussian filters and weights for each Gaussian filter in the multiscale retinex are then determined using a visual contrast measure and the maximum color difference of the color patches in the Macbeth color checker. The visual contrast measure is obtained based on the product of the local standard deviation and locally averaged luminance of the image. Meanwhile, to evaluate the halo artifacts generated in large uniform regions that abut to form a high contrast edge, the artifacts are evaluated based on the maximum color difference between each color of the pixels in a patch in the Macbeth color and the averaged color in CIELAB standard color space. When considering the color difference for halo artifacts, the parameters for the Gaussian filters and weights representing a higher visual contrast measure are determined using test images. In addition, to reduce the induced graying-out, the chroma of the resulting image is compensated by preserving the chroma ratio of the input image based on the maximum chroma values of the sRGB color gamut in the lightness–chroma plane. In experiments, the proposed method is shown to improve the local contrast and saturation in a natural way.