Abstract Acne is a skin disease mainly caused by bacteria, the hair follicles exposed to oil, and dying skin cells. These sometimes trigger whiteheads, blackheads or pimples, usually on the neck, arms, arm and back of shoulders. Acne in adolescents is the most severe, even though it affects people of any generation. Doctors can easily detect acne by seeing a patient's skin, but automatic acne detection is not easy for machines. Deep learning (DL) approaches have been quite successful for various aspects like classification and detection of objects in real life. This paper proposes an enhanced DL CNN model with the Leaky ReLU activation function. DermNet NZ's facial acne images dataset is used for the experiments. Three different techniques‐ K‐Means, Texture Analysis and HSV Model‐Based Segmentation, are applied for image segmentation to extract the acne region from skin images. After applying all the above image segmentation methods five times for each method, output images from K‐Means and HSV (5 + 5 images) are collected and combined with the dataset. Using that dataset, one SVM model using Scikit‐learn and two CNN models‐ one with the ReLU activation function and another with the LeakyReLU activation function, is trained. Out of these three models, the proposed CNN (LeakyReLU) model achieved a 97.54% accuracy.

In this article, the authors propose an adaptive frequency median filter (AFMF) to remove the salt and pepper noise. AFMF uses the same adaptive condition of adaptive median filter (AMF). However, AFMF employs frequency median to restore grey values of the corrupted pixels instead of the median of AMF. The frequency median can exclude noisy pixels from evaluating a grey value of the centre pixel of the considered window, and it focuses on the uniqueness of grey values. Hence, the frequency median produces a grey value closer to the original grey value than the one by the median of AMF. Therefore, AFMF outperforms AMF. In experiments, the authors tested the proposed method on a variety of natural images of the MATLAB library, as well as the TESTIMAGES data set. Additionally, they also compared the denoising results of AFMF to the ones of other state‐of‐the‐art denoising methods. The results showed that AFMF denoises more effectively than other methods.

Visibility of outdoor images is usually limited due to haze, dust, smoke and other particles in air. Visibility limit can cause many difficulties for activities of transport, rescue, oceanography etc. Hence, image dehazing is very necessary. In this paper, we propose a single image dehazing method based on combination of adaptive histogram equalization, HSV color model and linearization of Gamma correction. In the experiments, we test the proposed method on hazy images of the TAU dataset. To assess dehazing quality, we utilize NIQE metric and compare to other dehazing methods. The results confirm that the proposed method dehazes effectively and can compete with other state-of-the-art dehazing methods.

Understanding of real scenes is an important task in augmented reality (AR). Identifying and comprehension of texts from real scene images is useful in implementing robust AR devices. Therefore, improving the quality of text images for better readability is very important and text images deconvolution is useful to increase the accuracy of AR pattern recognition algorithms. In this work, we propose an estimation method for the filtering operator within total variation deconvolution model. This method is applied for the texts image deconvolution problem from natural images. In the experiments, we use NIQE score - the blind quality assessment metric - to assess the deconvolution quality. We further compare the proposed method with other image deconvolution models such as the blind deconvolution, the Lucy and the Wiener methods. The experimental indicate that our deconvolution method works effectively for texts enhancement across different scenes with high quality results.