A fast Multi-scale Retinex algorithm using dominant SSR in weights selection

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).

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.

In this paper, It focuses on few out of many Retinex based method for Image Enhancement. Retinex is basically a concept of capturing an image in such a way in which a human being perceives it after looking at an object at the place with the help of their retina (Human Eye) and cortex (Mind). On the basis of Retinex theory, we can say an image as a product of illumination and reflectance from the object. Retinex focuses on dynamic range and color constancy of an image. There are various methods proposed by various researchers till date which use Retinex for image contrast enhancement. In this paper, we will discuss Single Scale Retinex (SSR), Multi-Scale Retinex (MSR), Improved Retinex Image Enhancement (IRIE), MSR improvement for night time Enhancement (MSRINTE) and Retinex Based Perceptual Contrast Enhancement in image using luminance adaptation (RBPCELA).

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.

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.

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.

This paper describes an efficient image enhancement method based on the multi-scale retinex (MSR) theory for applying to a motion picture pre-processing. The proposed method uses a multi-rate x-y separable Gaussian filter with a polyphase structure and conducts illumination domain MSR composition. Therefore, The processing amount is drastically reduced to 4-order less than that of original MSR. As enhancing dark and bright areas in an image is the main pre-processing purpose, the method modifies an enhancement function so as to improve color rendition of bright areas on an image. Although our approach aims at simple implementation, the proposed method shows similar performance to the original one and better performance than those of histogram equalization approaches.

In this paper, a fast image enhancement algorithm based on multi-scale retinex in HSV colour model is presented. The proposed algorithm produces the result similar to the one which uses a nonlinear processing in the HSV colour model, but with less computational cost. It uses linear dependencies of RGB colours from the V-component of HSV model. Additionally, to speed up the images processing and enhance the local contrast is suggested to perform multi-scale retinex (MSR) computation only in the low-frequency area obtained by the wavelet transform. Experimental research was performed on more than 100 colour images having non-uniform brightness. Different algorithms based on retinex technology were implemented and their performance was compared. The proposed way of output image colour formation allows reducing processing time by 30%-75%, depending on the image size. The experimental data show that the usage of the wavelet transform in proposed MSR algorithm additionally leads to 2-2.8 times increase in processing speed.

The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the driver’s attention becomes distracted, even for a short period of time. For the purpose of alerting the driver and elevating his or her safety, in this paper we propose two components for any modern vision-based Advanced Drivers Assistance System (ADAS). These two components work separately for the single purpose of alerting the driver in dangerous situations. The purpose of the first component is to ascertain that the driver would be in a sufficiently wakeful state to receive and process warnings; this is the driver drowsiness detection component. The driver drowsiness detection component uses infrared images of the driver to analyze his eyes’ movements using a MSR plus a simple heuristic. This component issues alerts to the driver when the driver’s eyes show distraction and are closed for a longer than usual duration. Experimental results show that this component can detect closed eyes with an accuracy of 94.26% on average, which is comparable to previous results using more sophisticated methods. The purpose of the second component is to alert the driver when the driver’s vehicle is moving around larger vehicles at dusk or night time. The large vehicle detection component accepts images from a regular video driving recorder as input. A bi-level system of classifiers, which included a novel MSR-enhanced KAZE-base Bag-of-Features classifier, is proposed to avoid false negatives. In both components, we propose an improved version of the Multi-Scale Retinex (MSR) algorithm to augment the contrast of the input. Several experiments were performed to test the effects of the MSR and each classifier, and the results are presented in experimental results section of this paper.

A novel compressing method of high dynamic range image based on fast integrated surround Retinex model is proposed in this paper. The proposed method has two novelties. First, multiscale surround images are integrated to a single surround field, which is applied to center/surround single-scale Retinex (SSR) model. The method reduces the "banding artifact" seen in normal SSR and simplifies the complicated computational steps in conventional multiscale Retinex. Second, the Gaussian pyramid method is introduced to cut the computation time for generating a large-scale surround by tracing a "reduction" and "expansion" sequences using down and up sampling followed by linear interpolation. The computational expense is dramatically saved less than 1/100 for getting a surround by Gaussian convolution with large kernel size. The proposed model worked well in compressing the dynamic range and improving the visibility in heavy shadow areas of natural color images while preserving pleasing contrast.

With the ability of imaging the temperature distribution of body, infrared imaging is promising in diagnostication and prognostication of diseases. However the poor quality of the raw original infrared images prevented applications and one of the essential problems is the low contrast appearance of the imagined object. In this paper, the image enhancement technique based on the Retinex theory is studied, which is a process that automatically retrieve the visual realism to images. The algorithms, including Frackle-McCann algorithm, McCann99 algorithm, single-scale Retinex algorithm, multi-scale Retinex algorithm and multi-scale Retinex algorithm with color restoration, are experienced to the enhancement of infrared images. The entropy measurements along with the visual inspection were compared and results shown the algorithms based on Retinex theory have the ability in enhancing the infrared image. Out of the algorithms compared, MSRCR demonstrated the best performance.

Automatic Face Enhancement Technique using Sigmoid Normalization based on Single Scale Retinex (SSR) Algorithm

In view of the defects of the classical Retinex image enhancement algorithm, such as poor texture detail retention, halo and over enhancement, and abrupt tone, this paper proposes a low illumination color image enhancement algorithm based on Gabor filter and retinex theory. Firstly, the brightness I component of the image is extracted in the HSI color space, and then the image with the brightness component is enhanced by MSRCR (the color-recovered retinex algorithm). At the same time, the image is enhanced by SSR (the single-scale retinex algorithm) based on Gabor filter in RGB color space, and the image with better texture and edge details is obtained. Finally, the final enhanced image is obtained by weighted fusion. The algorithm is compared with SSR, MSR (the multi-scale retinex algorithm) and MSRCR. The experimental results show that the image information processed by this algorithm is richer in color, hue and the color is closer to the original image, and it effectively reduces the occurrence of halo and over-enhancement. This algorithm can enhance the image of some low illumination color images, and the visual effect of the enhanced image is relatively peaceful.

The images of oral potentially malignant disorders (OPMDs) frequently encounter problems related to visual quality and image distortion, which may lead to serious misdiagnosis and missed diagnosis. This study is to explore the auxiliary effects of different optical image enhancement algorithms on the object detection of OPMDs lesions. Digital images of OPMDs were collected, including white plaques, white stripes, and erosive lesions. The dataset was divided into a training set (6,488 images) and a validation set (2,592 images). Original images were processed using multiscale retinex (MSR), adaptive histogram equalization (AHE), and adaptive contrast enhancement (ACE), respectively. Object detection models based on You Only Look Once version 8 (YOLOv8) were used for lesion detection, and the diagnostic performance was evaluated using 328 images taken at different times. The model performance in the MSR-enhanced image set was superior to that in the original image set, with total accuracy increased for all three lesion types, and the sensitivity of complete correct recognition for complex multi-lesion images improved. Models trained with AHE and ACE preprocessing showed reduced diagnostic performance. Image enhancement algorithms can enhance the visual quality of OPMDs images, and the MSR algorithm is capable of strengthening the object detection ability in the computer vision model. This study provides an approach to reduce the misdiagnosis and missed diagnosis of OPMDs lesions in object detection model.

<div class="section abstract"><div class="htmlview paragraph">Accurately detecting lane lines remains a challenging task, especially with low-quality cameras due to the complex environment such as haze, uneven lighting, and shadows of actual roads. Despite numerous studies, lane line detection algorithms are still required to be improved for practical applications. In this work, we propose a new lane detection method that incorporates the brightness estimation concept of the single-scale retinex (SSR) algorithm into the dark channel prior (DCP) algorithm for image preprocessing. The improved DCP algorithm is used to estimate the atmospheric light intensity and remove haze noise, while simultaneously enhancing image contrast to reduce the difficulty of lane detection, especially under uneven lighting conditions, then followed by perspective transformation and HSV color space (hue, saturation, value) conversion, and finally, lane line recognition and tracking are performed using sliding windows and histogram statistics. Experimental results demonstrate that our method achieves high accuracy and robustness in lane line detection, especially under uneven lighting conditions, compared to existing methods. The proposed method can effectively handle complex situations such as haze, uneven lighting, and shadows, making it feasible for practical applications.</div></div>