In this paper, a new approach is presented for improving image quality. It provides a new outlook on how to apply the enhancment methods on images. Image enhancement techniques may deal with the illumination, resolution, or distribution of pixels values. Issues such as the illumination of the scene and reflectance of objects affect on image captures. Generally, the pixels value of an image is proportional to the illumination of point in the scene and the reflectance of the object. Indeed, the captured image is the results of illumination and reflectance of the object. Hence, impairment of the image may be due to each of the illumination or reflectance component. In this paper, it is shown that various types of impairments have different effects on the illumination and reflectance of image components. Studies showed that effects of image impairment on one of its components are more than on the other component depending on the type of impairment. Unlike conventional methods which do enhancement process on the original image for any type of impairment, in this paper it is to reduce the impairement effects from image components. Results of this research show that image enhancement based on the proposed method has better results compared to applying enhancement methods on original image.

In this paper, a method is proposed to automatically select reference image in histogram matching. Histogram matching is one of the simplest spatial image enhancement methods which improves contrast of the initial image based on histogram of the reference image. In the conventional histogram matching methods, user should perform several experiments on various images to find a suitable reference image. This paper presents a new method to automatically select the reference image. In this method, images are converted from RGB to HSV, and the illumination (V) components are considered to select the reference image. The appropriate reference image is selected using a similarity measure via measuring the similarity between the histograms of the initial image and histograms of the images in the data base. Indeed, an image with similar histogram to the histogram of the original images is more appropriate to choose as the reference image for histogram matching. Results in this research indicate superiority of the proposed approach, compared to other existing approaches, in image enhancement via histogram matching. In addition, the user would have no concern in selecting an appropriate reference image for histogram matching in the proposed approach. This approach is applicable to both RGB and gray scale images.

Biometric-based techniques have emerged as the most promising option for individual recognition. This task is still a challenge for computer vision systems. Several approaches to adult image recognition, which include the deep neural network and traditional classifier, have been proposed. Different image condition factors such as expressions, occlusion, poses, and illuminations affect the facial recognition system. A reasonable amount of illumination variations between the gallery and probe images need to be taken into account in adult image recognition algorithms. In the context of adult image verification, illumination variation plays a vital role and this factor will most likely result in misclassification. Different architectures and different parameters have been tested in order to improve the classification’ s accuracy. This proposed method contains four steps, which begin with Fuzzy Deep Neural Network Segmentation. This step is employed in order to segment an image based on illumination intensity. Histogram Truncation and Stretching is utilized in the second step for improving histogram distribution in the segmented area. The third step is Contrast Limited Adaptive Histogram Equalization (CLAHE). This step is used to enhance the contrast of the segmented area. Finally, DCT-II is applied and low-frequency coefficients are selected in a zigzag pattern for illumination normalization. In the proposed method, AlexNet architecture is used, which consists of 5 convolutional layers, max-pooling layers, and fully connected layers. The image is passed through a stack of convolutional layers after fuzzy neural representation, where we used filter 8 × 8. The convolutional stride is fixed to 1 pixel. After every convolution, there is a subsampling layer, which consists of a 2×2 kernel to do max pooling. This can help to reduce the training time and compute complexity of the network. The proposed scheme will be analyzed and its performance in accuracy and effectiveness will be evaluated. In this research, we have used 80, 400 images, which are imported from two datasets-the Compaq and Poesia datasets-and used images found on the Internet.

We propose a new approach for image enhancement, denoising and restoration, using an anisotropic diffusion based on P-M model and L.V and al. equation, replacing the gradient by motion by mean curvature to detect noise direction for each degraded pixel locally, applying the gradient in Gaussian kernel term to restore the degraded pixels and adding a time term supporting the restoration process. For execution progress, the numerical discretization for the terms of PDE modeling (obtained by the approximation by difference finite volumes finite method, Taylor method and Simpsons improved method), concludes an algorithm treats noised image regardless the noise type (salt-pepper or Gaussian or speckle) better than other filters based whether on anisotropic diffusion or total, shown in the experimental results (using MATLAB program), and demonstrated through PSNR and SSIM.

Background: Delivering the radiation dose to the target volume and minimizing the dose to normal tissues are the main objectives in radiotherapy. The aim of our study is to enhance the contrast of the portal image to increase the accuracy of delineation of the organs in the irradiation field. Methods: The software was written based on local enhancement of the pixel values in image matrix. The portal images were digitized by charged coupled device (CCD) in compatible format to be read with this program. This program was applied as an m-file in MATLAB imaging tool box to the matrices of the portal images. The imaging parameters before and after application of the program were compared. Results: The quantitative information of images was obtained. Analysis of the mean and standard deviations of the results has shown that the difference of the criteria between two groups of the images is significant (p< 0.01). In qualitative analysis, final images scores were based on “special weight “. The result of this test confirms the superior quality of the post-processed images from the professional view point. Conclusion: Superiority of final images within the three studied parameters by the experts (superiority of lung image, superiority of thorax and its soft tissue images) can be used to increase the accuracy of the treatment set up and decrease the probability of normal tissue complications.

This paper presents a new filtering approach based on fuzzy-logic which has high performance in mixed noise environments. This filter is mainly based on the idea that each pixel is not allowed to be uniformly fired by each of the fuzzy rules. In the proposed filtering algorithm, the rule membership functions are tuned iteratively in order to preserve the image edges. Several test experiments were performed in order to hlight the merit of the proposed method. The results are very promising and indicating the high performance of the proposed filter in image restoration compared with those of the filters which have been recently cited in the image processing literature.

Image taking  using unmanned aerial vehicles (UAVs) for monitoring and assessing the existing conditions  is one of the most prevalent applications in surveying. Despite its significant advantages, this approach also faces some challenges. Improper camera settings during image capturing, adverse weather conditions, and changes in lighting are the primary factors that reduce the quality of the captured images. Generally, the proposed methods for brightness enhancement can be categorized into two groups: traditional methods, which rely on histograms, and modern methods, including neural networks and deep learning, which have increasingly attracted the attention of researchers. The objective of this study is to evaluate the performance of deep learning methods in enhancing the brightness of the aerial images. These images often exhibit inadequate quality due to reduced visual details and the consequent loss of spectral information. Such deficiencies negatively impact the quality of the spatial products, such as orthophotos and digital surface models. Improving brightness and recovering spectral information have a direct and significant influence on the quality of these spatial products. To this end, the study examines three different deep learning methods that have demonstrated superior performance in the previous researches for enhancing aerial image brightness. The optimal method is selected based on 10 brightness evaluation metrics. The evaluated data consists of the aerial images captured from two different regions, characterized by areas with significant visual detail reduction and loss of spectral information due to poor lighting conditions. The results reveal hidden features in shadowed regions and areas with excessive brightness and high environmental reflection, which are not easily discernible by the naked eye. This is achieved by recovering spectral information through increasing the contrast between the digital values of the pixels in these regions. The best-performing method achieves structural similarity index (SSIM) scores of 0.92 and 0.96 for the two datasets, respectively. SSIM is one of the most critical evaluation metrics among the 10 criteria utilized in this study.