An Image Enhancement Based CDVS Matching Algorithm

Performing image retrieval in large image database is becoming popular with the development of multimedia technology. Recently, ISO/IEC moving pictures experts group (MPEG) drafts compact descriptors for visual search (CDVS) to support the related applications. The state-of-the-art feature selection strategy in CDVS adopts a priori relevance measure to guide the selection. However, this method ignores the gradient information of the described feature. In this paper, we adopt CDVS to address traffic vehicle search in large database. After detailedly analyzing to the local descriptor, we firstly define local descriptor distinctive degree based on the gradient quantity and the spatial gradient distribution energy of the feature. Then we propose our adaptive feature selection method by combing feature distinctive degree and the priori information. The proposed method is proven to be better than the standard algorithm in CDVS and it almost introduces 10% mean average precision (MAP) and top match rate increase in low bit rate mode.

The Moving Picture Experts Group's Compact Descriptors for Visual Search (MPEG's CDVS) intends to standardize technologies in order to enable an interoperable, efficient, and cross-platform solution for internet-scale visual search applications and services. Among the key technologies within CDVS, we recall the format of visual descriptors, the descriptor extraction process, and the algorithms for indexing and matching. Unfortunately, these steps require precision and computation accuracy. Moreover, they are very time-consuming, as they need running times in the order of seconds when implemented on the central processing unit (CPU) of modern mobile devices. In this paper, to reduce computation times and maintain precision and accuracy, we re-design, for many-cores embedded graphical processor units (GPUs), all main local descriptor extraction pipeline phases of the MPEG's CDVS standard. To reach this goal, we introduce new techniques to adapt the standard algorithm to parallel processing. Furthermore, to reduce memory accesses and efficiently distribute the kernel workload, we use new approaches to store and retrieve CDVS information on proper GPU data structures. We present a complete experimental analysis on a large and standard test set. Our experiments show that our GPU-based approach is remarkably faster than the CPU-based reference implementation of the standard, and it maintains a comparable precision in terms of true and false positive rates.

Self-supervised network for low-light traffic image enhancement based on deep noise and artifacts removal

Acoustic images captured by side scan sonar are normally affected by speckle noise for which the enhancement is required in different domain. The underwater acoustic images obtained using sound as a source, basically contain seafloor, sediments, living and non-living resources. The Multiresolution based image enhancement techniques nowadays play a vital role in improving the quality of the low resolution image with repeated patterns. Image pyramid is the representation of an image at various scales. In this work, a three level Gaussian and Laplacian pyramids are constructed to represent the image in different resolution. The multiscale representation requires different filters at different scales. The contrast of each image in Gaussian and Laplacian pyramids are improved by applying both histogram equalization and unsharp masking method. The sharpened images are used to reconstruct the enhanced image. The performance measure, peak signal to noise ratio proves that the unsharp masking method applied to difference images of Laplacian pyramid outperforms the other image enhancement methods.

Image enhancement methods were used to modify the images that were accepted as visual perception. Image enhancement methods mainly used for image contrast. Histogram compensation, used in the form of the cumulative distribution function (CDF), is one of the challenging factors in image processing. The goal of the enhancement is to improve the structural look of an image without impairing the input image. The enhancement techniques make it easier to identify key features by removing noise and other artifacts from an image. This article discusses studies to implement image enhancement in specific areas such as satellite imagery, infrared imagery, medical imagery and digital entertainment imagery, as well as information from the image enhancement method used. Keywords: Image enhancement, AHE, Histogram Equalization, CLAHE, spatial domain image, frequency domain method, etc. Cite this Article: Manish Sharma, Ashish Dubey, Prashant Badal. Review Paper on Image Enhancement using Various Methods. International Journal of Image Processing and Pattern Recognition. 2020; 6(1): 27–35p.

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.

Poor visibility not only affects the consumer photography but also computer vision application. Low-light image enhancement explains the image details or perception of information in images for human viewers and understanding of imagery. Using the pixel values existing in the digital image, the details of low-light images are boosted under low illumination. The primary purpose of enhancing low-vision image is to increase visibility of the image. Dealing with noise is a significant challenge while enhancing. Previous studies have offered a variety of approaches for enhancing low-vision images. Low contrast, low brightness, severe noise, dark colors are the issues with image enhancing in low-light situations. Monochromatic atmospheric scattering model is used to form proposed absorption light scattering model. The concealed details and hidden features were reproduced with sufficient and homogeneous illumination. This strategy will help to achieve high result in terms of structure, details and local feature similarity. Here, some mathematical formulas were used to produce brightest image, which is produced from monochromatic ASM and absorption light scattering model. When we compare absorption light scattering method with other techniques, it will produce better result. This strategy highlights major details of absorption light scattering model. This method will produce better result in terms of contrast, brightness, sharpness, etc.

Despite recent advances in contrast enhancement, it remains difficult for existing methods to simultaneously achieve consistent improvements in image brightness and contrast in both low-light and normal-light images. To address this issue, we revisit 2D histogram equalization methods and extend them to accommodate more diversified and poor lighting conditions. The extension is based on the observation that the contrast needs to be improved by increasing the intensity difference between spatially neighboring pixels, while the degree of increase should be proportional to the difference in their reflectances. On the basis of this observation, we propose to embed the inter-pixel contextual information of image reflectance into the 2D histogram of intensity co-occurrence. An intensity mapping function can thus be derived by solving optimization problems formulated with the 2D histogram, leading to two novel contrast enhancement methods. Qualitative and quantitative evaluations on more than 600 images showed that the proposed methods are superior to state-of-the-art contrast enhancement methods. It was also shown that the reflectance of an image provides important visual information on the significance and objectness of local image areas. Using this reflectance as a clue, the degree of contrast enhancement can be adaptively derived to achieve sufficient brightness improvement in low-light images and to avoid excessive enhancement in normal-light images.

Image enhancement can be considered as one of the fundamental processes in image analysis. The goal of contrast enhancement is to improve the quality of an image to become more suitable for a particular application. Till today, numerous image enhancement methods have been proposed for various applications and efforts have been directed to further increase the quality of the enhancement results and minimize the computational complexity and memory usage. In this paper, an image enhancement methods based on Histogram Equalization (HE) was studied. This paper presents an exhaustive review of these studies and suggests a direction for future developments of image enhancement methods. Each method shows the owned advantages and drawbacks. In future, this work will give the direction to other researchers in order to propose new advanced enhancement techniques.

This study introduces an innovative unsupervised low-light image enhancement method, termed GDLLE. The key innovation lies in decoupling the luminance and content representations of an image, thereby establishing a bidirectional mapping between low-light and normal-light images, which significantly enhances the visual quality of low-light images. In contrast to traditional supervised learning approaches that rely on paired datasets, the GDLLE method does not require paired low-light and normal-light image pairs. Instead, it enhances low-light images by extracting luminance masks and content information from unpaired images. Furthermore, the study incorporates an adaptive normalization strategy and a multi-scale enhancement framework to optimize both the global structure and local details of the image. To further improve the enhancement performance, a self-supervised mechanism based on cyclic consistency is introduced, which leverages cyclic consistency loss and self-feature preservation loss to ensure the quality of the generated images. Experimental results demonstrate that the GDLLE method outperforms existing approaches on several benchmark datasets, compared with the baseline models, GDLLE has achieved an improvement of 0.46 dB in the PSNR metric, validating its effectiveness and superiority in the unsupervised low-light image enhancement task. This research offers a novel solution in the field of low-light image enhancement, with promising potential for superior image quality improvement in practical applications.

In the enhancement of low-light images, noise, low contrast, color distortion, and loss of details have remained great issues that have not been overcome by computer vision. The traditional products like gamma correction, CLAHE, and histogram equalization are used to improve the brightness, but they are incapable of finding the balance between boosting and reducing noises. Recent Retinex, GAN and encoder decoder processes show improvements but are incapable of preserving fine details of natural color restoration. The current paper suggests a hybrid model that comprises SwinIR-denoising, UNet reconstruction using attention-focused skip connections, and guided filtering with Laplacian decomposition. It is stated that the model trained in the LOL dataset achieved PSNR of 20.21 dB, SSIM of 0.774 and MSE of 0.0134 in a 58 -minute training period and thus it has good brightness correction abilities, detail preservation and realistic texture reproduction in low-light images.

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.

Image enhancement is the task of applying certain alterations to an input image such as to obtain a more visually pleasing image. The alteration usually requires interpretation and feedback from a human evaluator of the output resulting image. Therefore, image enhancement is considered a difficult task when attempting to automate the analysis process and eliminate the human intervention. Furthermore, images that do not have uniform brightness pose a challenging problem for image enhancement systems. Different kinds of histogram equalization techniques have been employed for enhancing images that have overall improper illumination or are over/under exposed. However, these techniques perform poorly for images that contain various regions of improper illumination or improper exposure. In this paper, we introduce new human vision model based automatic image enhancement techniques, multi-histogram equalization as well as local and adaptive algorithms. These enhancement algorithms address the previously mentioned shortcomings. We present a comparison of our results against many current local and adaptive histogram equalization methods. Computer simulations are presented showing that the proposed algorithms outperform the other algorithms in two important areas. First, they have better performance, both in terms of subjective and objective evaluations, then that currently used algorithms on a series of poorly illuminated images as well as images with uniform and non-uniform illumination, and images with improper exposure. Second, they better adapt to local features in an image, in comparison to histogram equalization methods which treat the images globally.

이전 연구에서 퍼지 C-Means의 중심 데이터로 이루어진 보조 데이터를 이용한 히스토그램 등화기법을 제안하였다. 보조 데이터를 이용한 히스토그램 등화기법은 사용하는 참조 집합의 크기에 따라 화자 식별 성능에 영향을 받는다. 그러나 인식 시점에서 최적의 파라미터를 찾기는 어렵다. 이 문제를 해결하기 위해 본 논문에서는 화자 식별을 위한 다수 투표 방식에 기반을 둔 보조 데이터를 이용한 히스토그램 등화기법을 제안한다. 다수 투표 기반의 제안한 방법은 여러 종류의 보조 데이터를 이용한 히스토그램 등화기법으로 입력 음성을 분류한다. 본 연구에서 제안한 방법을 CMN(Cepstral Mean Normalization), MVN(Mean and Variance Normalization), HEQ(Histogram Equalization)와 같은 기존의 특징 정규화 방법 및 보조 데이터를 이용한 히스토그램 등화기법과 비교한다. In a previous work, we proposed a novel approach of histogram equalization using a supplement set which is composed of centroids of Fuzzy C-Means of the background utterances. The performance of the proposed method is affected by the size of the supplement set, but it is difficult to find the best size at the point of recognition. In this paper, we propose a histogram equalization using a supplement set for majority voting based speaker identification. The proposed method identifies test utterances using a majority voting on the histogram equalization methods with various sizes of supplement sets. The proposed method is compared with the conventional feature normalization methods such as CMN(Cepstral Mean Normalization), MVN(Mean and Variance Normalization), and HEQ(Histogram Equalization) and the histogram equalization method using a supplement set.

CCTV (Closed-circuit Television) is a surveillance camera that is installed on roads and in various rooms around the world with the aim of reducing crime and increasing public safety. Often, the image quality of CCTV has poor results, one of the causes is noise. This condition causes the information contained is sometimes not well conveyed. Image enhancement is a part of image processing which has the aim of improving image quality by manipulating image parameters. The application of this research is to sharpen the quality of screen capture on CCTV. The method applied is histogram equalization which can improve image quality. Histogram equalization method will be applied using Google Colab because its use has good portability.