Detail Enhancement Algorithm Research Articles

一种基于图像分层的红外图像目标细节增强算法

一种基于图像分层的红外图像目标细节增强算法

Detail enhancement of infrared image based on bi-exponential edge preserving smoother

In this paper, a new infrared image detail enhancement algorithm has been raised. The original infrared image has a wide dynamic range of 12- or 14-bit.This suppresses the human observation range of 8-bit. Usually the original infrared image needs to be compressed and gray-scale remapped for displaying. However, the normal way of doing this cannot give a better visual effect for the human observer. In this case, detail enhancement algorithms of infrared image occur. Modern detail enhancement algorithms can extract the detail information from an original infrared image and separate the image into different layers, and each layer will processed with different strategy. Although good performance has been proved for these algorithms, there are still certain deficits such as too much computational time, low working efficiency, hard application flexibility and so on. Under this circumstances, we propose this new algorithm to overcome these problems. This algorithm uses a two dimensional convolution to separate the detail information from an original infrared image, and turn the original image into the detail layer and the base layer. The detail information will be enhanced without any unwanted artifacts. During the detail extraction, We speed up the whole computational process by transforming the two dimensional convolution into two one dimensional convolutions, and then express the one dimensional convolution with the iterative computation. After adding the enhanced detail layer back to the histogram equalized base layer, the visual quality of the original image can be improved. This algorithm not only gives better detail enhancement performance, but also reduces the computational time. Figures and data tests show the priority of our suggestions.

Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement

We address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled simultaneously. Moreover, existing algorithms cannot handle face images well as low-resolution face images do not have much texture which is especially critical for deblurring. In this paper, we propose an effective algorithm by utilizing the domain-specific knowledge of human faces to recover high-quality faces. We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image. However, the CNN based method cannot handle image details well. We further develop a novel exemplar-based detail enhancement algorithm via facial component matching. Extensive experiments show that the proposed method outperforms the state-of-the-art algorithms both quantitatively and qualitatively.

An improved adaptive detail enhancement algorithm for infrared images based on guided image filter

ABSTRACTDetail enhancement algorithms are important for raw infrared images to improve their overall contrast and highlight important information in them. To solve the problems that current algorithms like GF&DDE have, an improved adaptive detail enhancement algorithm for infrared images based on a guided image filter is proposed in this paper. It chooses the threshold for the base layer image adaptively according to the histogram statistical information and adjusts the mapping range of the histograms according to the dynamic range of the image. Besides, the detail layer is handled by a simpler adaptive gain control method to achieve the good detail enhancement effect. Finally, the base layer and the detail are merged according to the approximate proportion of the background and the details. Experimental results show that the proposed algorithm can adaptively and efficiently enhance different dynamic range images in different scenarios. Moreover, this algorithm has high real-time performance.

Color image detail enhancement based on quaternion guided filter

AbstractColor image enhancement is an active research field in image processing. Currently, many image enhancement methods are capable of enhancing the details of the color image. However, these methods only process the red, green and blue (RGB) color channels separately, which leads to color distortion easily. In order to overcome this problem, the paper presents an approach to integrate the quaternion theory into the traditional guided filter to obtain a quaternion guided filter (QGF). This method makes full use of the color information of an image to realize the holistic processing of RGB color channels. So as to preserve color information while enhancing details, this paper proposes a color image detail enhancement algorithm based on the QGF. Experimental results show that the proposed algorithm is effective in the applications of the color image detail enhancement, and enables image's edges to be more prominent and texture clearer while avoiding color distortion. Compared with the existing image enhancement methods, the proposed method achieves better enhancement performance in terms of the visual quality and the objective evaluating indicators.

A Detail Enhancement Algorithm of Infrared Images Based on Second-generation Wavelet Cumulative Probability

A Detail Enhancement Algorithm of Infrared Images Based on Second-generation Wavelet Cumulative Probability

A Detail Enhancement Algorithm of Infrared Images Based on Second-generation Wavelet Cumulative Probability

Aiming at the drawbacks of poor contrast, fuzzy edge and insufficient texture information widely existing in infrared images, a detail enhancement method of infrared images based on second-generation wavelet cumulative probability is proposed. Firstly, the algorithm decomposes infrared images for the second-generation wavelet mono-layer, and the cumulative probability distribution function is used to calculate the detail histogram equalization of the infrared images, then the histogram equalization is processed respectively. The comparison of several algorithms shows that the proposed method is outstanding in image detail in the digital detail enhancement of infrared image, and the noise suppression effect is good.

A color image enhancement algorithm based on quaternion representation of vector rotation

Detail enhancement of color images is required in many applications. Unsharp masking (UM) is the most classical tool for detail enhancement. Many generalizing UM approaches have been proposed, for example, the rational UM technique, the cubic unsharp technique, the adaptive UM technique and so on. For color images, these algorithms have three steps: (a) Implement the color2gray step; (b) design an extracting method of high frequency information (HFI) based on the luminance component (LC); (c) complete the enhancing process utilizing the HFI. However, using only the HFI of the LC may lose the HFI of the chrominance component (CC). This paper proposes a quaternion based detail enhancement algorithm to extract details of the color image using both of the luminance and CCs. The proposed algorithm is designed to address three tasks: (1) designing an extraction method of the color high frequency information (CHFI) based on quaternion description of the 3D vector rotation; (2) performing an effective fusion strategy of the CHFI and the gray high frequency information (GHFI); (3) designing a quaternion based measure method of the local dynamic range, based on which the enhancement coefficients of the proposed algorithm can be determined. The performance of the proposed algorithm compares favorably with many other similar enhancement algorithms. The eight parameters can be adjusted to control the sharpness to produce the desired results, which makes the proposed algorithm practically useful.

Content Adaptive Image Detail Enhancement

Detail enhancement is required by many problems in the fields of image processing and computational photography. Existing detail enhancement algorithms first decompose a source image into a base layer and a detail layer via an edge-preserving smoothing algorithm, and then amplify the detail layer to produce a detail-enhanced image. In this letter, we propose a new <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm based detail enhancement algorithm which generates the detail-enhanced image directly. The proposed algorithm preserves sharp edges better than an existing <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm based algorithm. Experimental results show that the proposed algorithm reduces color distortion in the detail-enhanced image, especially around sharp edges.

Dynamic range compression and detail enhancement algorithm for infrared image.

For infrared imaging systems with high sampling width applying to the traditional display device or real-time processing system with 8-bit data width, this paper presents a new high dynamic range compression and detail enhancement (DRCDDE) algorithm for infrared images. First, a bilateral filter is adopted to separate the original image into two parts: the base component that contains large-scale signal variations, and the detail component that contains high-frequency information. Then, the operator model for DRC with local-contrast preservation is established, along with a new proposed nonlinear intensity transfer function (ITF) to implement adaptive DRC of the base component. For the detail component, depending on the local statistical characteristics, we set up suitable intensity level extension criteria to enhance the low-contrast details and suppress noise. Finally, the results of the two components are recombined with a weighted coefficient. Experiment results by real infrared data, and quantitative comparison with other well-established methods, show the better performance of the proposed algorithm. Furthermore, the technique could effectively project a dim target while suppressing noise, which is beneficial to image display and target detection.

A New High-Dynamic-Range Compression and Detail Enhancement Algorithm for Infrared Images

Infrared images have the high dynamic range characteristic, generally digitized to 14 bit. While most display devices can only show 8 bit images, so high bit-wide infrared image signal should be compressed to low bit-wide display data without losing the important detail information. A new high-dynamic-range compression and detail enhancement algorithm for infrared images is presented in this paper. In the proposed algorithm, the original infrared image is separated into the low-frequency base component and the high-frequency detail component. Then the base component is compressed with the simple gray level linear mapping, and the detail component is enhanced with the S-curve transformation. Finally, the two components are combined to get the 8 bit enhanced image. The experimental results show that the proposed algorithm can achieve the dynamic range compression while effectively preserve and enhance the local detail information.

K-means 알고리즘을 사용한 칼라 동영상 링잉 노이즈 감쇄 방법의 개선

In this paper, we proposed the improved Advanced Detail Enhancement algorithm that improve the blurring by the lossy compression with CODEC and reduce the ringing artifacts in restoration. The conventional algorithm needs much amount of the process by the use of RGB color space. To improve this, we only used the luminance value in YCbCr color space. We verified that the performance of the improved algorithm with Y color value, the luminance value, is equal to the conventional algorithm with RGB color value and that the operation time of the improved is shorter about 24% than the conventional through the measurement of the operation time with Kodak standard images.

一种红外图像细节增强和动态范围压缩处理算法

一种红外图像细节增强和动态范围压缩处理算法