Saibabu Arigela

Self-tunable transformation function for enhancement of high contrast color images

Abstract. A new image enhancement technique based on a self-tunable transformation function to improve the visual quality of images captured with low dynamic range devices in extreme lighting conditions is presented. This technique consists of four processes: histogram adjustment, dynamic range compression, contrast enhancement, and nonlinear color restoration. Histogram adjustment on each spectral band is performed to minimize the effect of illumination. Dynamic range compression is accomplished by a newly designed inverse sine nonlinear function that provides various nonlinear curvatures with an image dependent parameter. A nonlinear curve generated by this parameter is used to modify the intensity of each pixel in the luminance image. A nonlinear color restoration process based on the chromatic information and luminance of the original image is employed. The effectiveness of this technique is evaluated on various natural images and aerial images, and compared with other state-of the art techniques. A quantitative evaluation is performed by estimating the number of Harris corners and speeded up robust features on wide area motion imagery data. The application of the proposed algorithm on face detection is also demonstrated. The evaluation results demonstrate that the proposed method holds significant benefits for surveillance and security applications and also as a preprocessing technique for object detection and tracking applications.

An Adaptive and Non Linear Technique for Enhancement of Extremely High Contrast Images

In night time surveillance, there is a possibility of having extremely bright and dark regions in some image frames of a video sequence. A novel non linear image enhancement algorithm for digital images captured under such extremely non-uniform lighting conditions is proposed in this paper. The new technique constitutes three processes viz. adaptive intensity enhancement, contrast enhancement and color restoration. Adaptive intensity enhancement uses a specifically designed nonlinear transfer function which is capable of reducing the intensity of bright regions and at the same time enhancing the intensity of dark regions. Contrast enhancement tunes the intensity of each pixels magnitude based on its surrounding pixels. Finally, a linear color restoration process based on the chromatic information of the input image frame is applied to convert the enhanced intensity image back to a color image.

Visibility improvement of aerial imagery by a locally tuned nonlinear enhancement technique

This paper presents the effectiveness of locally tuned sine nonlinear (LTSN) image enhancement technique on poor contrast and low quality images that exhibit dark shadows and over exposed regions due to the limited dynamic ranges of imaging and display devices. We use a wide area motion imagery data [1] to evaluate the performance of the LTSN method. A quantitative evaluation is performed by estimating the number of Harris corners and Speeded Up Robust Features (SURF) in the enhanced images and compared the performance with other state of the art methods. The evaluation results confirms that the LTSN method can be applied as a preprocessing technique for object detection, tracking and recognition in wide area motion imagery.

Adaptive and nonlinear techniques for visibility improvement of hazy images

In outdoor video processing systems, the image frames of a video sequence are usually subjected to poor visibility and contrast in hazy or foggy weather conditions. A fast and efficient technique to improve the visibility and contrast of digital images captured in such environments is proposed in this paper. The image enhancement algorithm constitutes three processes viz. dynamic range compression, local contrast enhancement and nonlinear color restoration. We propose a nonlinear function to modify the wavelet coefficients for dynamic range compression and uses an adaptive contrast enhancement technique in wavelet domain. A nonlinear color restoration process based on the chromatic information of the input image frame is applied to convert the enhanced intensity image back to a color image. We also propose a model based image restoration approach which uses a new nonlinear transfer function on luminance component to obtain the transmission map. Experimental results show better visibility compared to those images enhanced with other state of art techniques.

Nonlinear technique for the enhancement of extremely high contrast images

An adaptive technique for image enhancement based on a specifically designed nonlinear function is presented in this paper. The enhancement technique constitutes three main processes-adaptive intensity enhancement, contrast adjustment, and color restoration. A sine function with an image dependent parameter is used to tune the intensity of each pixel in the luminance image. This process provides dynamic range compression by boosting the luminance of darker pixels while reducing the intensity of brighter pixels and maintaining local contrast. The normalized reflectance image is added to the enhanced image to preserve the details. The quality of the enhanced image is improved by applying a local contrast enhancement followed by a contrast stretch process. A basic linear color restoration process based on the chromatic information of the original image is employed to convert the enhanced intensity image back to a color image. The performance of the algorithm is compared with other state of the art enhancement techniques and evaluated using a statistical image quality evaluation method.

Neural Network Based Methodology for Automatic Detection of Whale Blows in Infrared Video

In this paper, we propose a new methodology based on neural networks to detect the presence of whale blows in infrared video. The algorithm is designed based on the spatial and temporal characteristics of whale blows. The first part of the algorithm consists of thresholding techniques that filter out the possible candidates to a group containing whale blows and certain textures on the sea. A novel thresholding technique called grid thresholding is proposed so that the detector is able to detect very small blows while keeping the number of false positives to a minimum. As the final part of the detection algorithm we have used a neural network to differentiate between whale blows and the various textures on the surface of the sea.

Enhancement of Hazy Color Images Using a Self-Tunable Transformation Function

Vision based outdoor mobile systems are very sensitive to infelicitous weather circumstances like hazy and foggy conditions. The acquisition of image frames in such an environment deteriorates the scene contrast and biases the color information. In order to recover the scene details, we propose a new method which takes a nonlinear approach, where the haze pixel intensity is manipulated effectively with a specially designed sine nonlinear function. This function is integrated with the optics based haze model to approximate the enhanced inverse transmission of the scene. The transformation function is composed with a variable parameter, which tunes automatically, to produce desired nonlinear mapping for each pixel while maintaining the local contrast. Unlike other state-of art haze removal techniques, which operates on local regions, proposed method operates on each pixel to eliminate the blocking artifacts and minimizes the processing complexity. Our experimental results with quantitative measures demonstrate that the proposed technique yields state-of-the-art performance on hazy images and is suitable to process a dynamic video scenes captured in adverse weather conditions.

Color image enhancement of low-resolution images captured in extreme lighting conditions

Security and surveillance videos, due to usage in open environments, are likely subjected to low resolution, underexposed, and overexposed conditions that reduce the amount of useful details available in the collected images. We propose an approach to improve the image quality of low resolution images captured in extreme lighting conditions to obtain useful details for various security applications. This technique is composed of a combination of a nonlinear intensity enhancement process and a single image super resolution process that will provide higher resolution and better visibility. The nonlinear intensity enhancement process consists of dynamic range compression, contrast enhancement, and color restoration processes. The dynamic range compression is performed by a locally tuned inverse sine nonlinear function to provide various nonlinear curves based on neighborhood information. A contrast enhancement technique is used to obtain sufficient contrast and a nonlinear color restoration process is used to restore color from the enhanced intensity image. The single image super resolution process is performed in the phase space, and consists of defining neighborhood characteristics of each pixel to estimate the interpolated pixels in the high resolution image. The combination of these approaches shows promising experimental results that indicate an improvement in visibility and an increase in usable details. In addition, the process is demonstrated to improve tracking applications. A quantitative evaluation is performed to show an increase in image features from Harris corner detection and improved statistics of visual representation. A quantitative evaluation is also performed on Kalman tracking results.

Intensity and Resolution Enhancement of Local Regions for Object Detection and Tracking in Wide Area Surveillance

Object tracking in wide area motion imagery is a complex problem that consists of object detection and target tracking over time. This challenge can be solved by human analysts who naturally have the ability to keep track of an object in a scene. A computer vision solution for object tracking has the potential to be a much faster and efficient solution. However, a computer vision solution faces certain challenges that do not affect a human analyst. To overcome these challenges, a tracking process is proposed that is inspired by the known advantages of a human analyst. First, the focus of a human analyst is emulated by doing processing only the local object search area. Second, it is proposed that an intensity enhancement process should be done on the local area to allow features to be detected in poor lighting conditions. This simulates the ability of the human eye to discern objects in complex lighting conditions. Third, it is proposed that the spatial resolution of the local search area is increased to extract better features and provide more accurate feature matching. A quantitative evaluation is performed to show tracking improvement using the proposed method. The three databases, each grayscale sequences that were obtained from aircrafts, used for these evaluations include the Columbus Large Image Format database, the Large Area Image Recorder database, and the Sussex database.

Shadow Removal for Illumination Invariant Face Detection in Hyperspectral Imagery

We present an adaptive visibility improvement technique that leverages the hyperspectral-bands to improve the local contrast and spatial resolution of shadow regions. The proposed technique is evaluated by determining the face-detection rate under various illuminations.