A hierarchical elastic computer-aided detection algorithm is proposed to automatically detect the horizon in an aerial image. A hierarchical strategy, including coarse-level detection and fine-level adjustment, is applied. First, the original image is blurred by a large-scale low-pass filter. Then, a Canny edge detector and Hough transform are successively utilized to find major edges in the image and identify lines associated with those major edges. The desired horizon is modeled by the resulting line that best satisfies certain criteria. By doing so, the general position of the horizon can be quickly detected at the coarse-level step. Since the horizon is often not a straight line, an elastic fine-level adjustment is applied to capture the precise curvature of the horizon. A quantitative performance metric is designed, and preliminary experimental results show the feasibility and reliability of the proposed algorithm.

This paper proposes a novel image fusion method that uses continuous Genetic Algorithm (CGA). Image fusion provides a single output image from a set of input images, captured from different viewing conditions, by different sensors, and at different times. Image fusion can be divided into three processing levels: pixel, feature and decision. Genetic algorithms are a nonlinear optimization technique that seeks the optimum solution by modifying the input vectors. In the pixel level image fusion (PLIF) process, the weights used for fusion need to be optimized to form a new composite image with extended information content. In this paper, we present a novel, entropy-based image fusion algorithm that uses CGA. Experimental results show that CGA based image fusion outperforms other point-rules based PLIF algorithms.

Recently, considerable research has been done to reduce the gap between direct observation of a scene and its recorded image. Typically the differences exist because of the high dynamic range compression capability of the human visual systems, and its capacity for color constancy. In this paper we define a new method that provides dynamic range compression, color consistency and color rendition for dark images with uniform and non-uniform illumination.

This paper proposes a novel wavelet based nonlinear image enhancement algorithm, including dynamic range compression, contrast enhancement and color restoration, for recorded images in non-uniform and uniform-dark lightning conditions. Dynamic range compression (DRC) has been applied by nonlinear function for enhancing dark and reducing the intensity of bright regions. The intensity has been tuned with contrast enhancement. The enhanced intensity has been converted to a color image with the help of color restoration.

A new wavelet-based image enhancement algorithm is proposed to improve performance of face detection in non-uniform lighting environment with high dynamic range. Wavelet transform is used for dimension reduction so that dynamic range compression with local contrast enhancement algorithm is applied only to the approximation coefficients. The normalized approximation coefficients are transformed using a hyperbolic sine curve which achieves dynamic range compression. Contrast enhancement is realized by tuning the magnitude of each coefficient with respect to its surroundings. The detail coefficients are also modified to prevent the edge deformation. Experimental results on the proposed algorithm show improvement on the performance of the Viola-Jones face detector when compared to other prominent enhancement techniques.

The last version of Land's (1986) retinex model for human vision's lightness and color constancy has been implemented and tested in image processing experiments. Previous research has established the mathematical foundations of Land's retinex but has not subjected his lightness theory to extensive image processing experiments. We have sought to define a practical implementation of the retinex without particular concern for its validity as a model for human lightness and color perception. We describe the trade-off between rendition and dynamic range compression that is governed by the surround space constant. Further, unlike previous results, we find that the placement of the logarithmic function is important and produces best results when placed after the surround formation. Also unlike previous results, we find the best rendition for a "canonical" gain/offset applied after the retinex operation. Various functional forms for the retinex surround are evaluated, and a Gaussian form is found to perform better than the inverse square suggested by Land. Images that violate the gray world assumptions (implicit to this retinex) are investigated to provide insight into cases where this retinex fails to produce a good rendition.

Direct observation and recorded color images of the same scenes are often strikingly different because human visual perception computes the conscious representation with vivid color and detail in shadows, and with resistance to spectral shifts in the scene illuminant. A computation for color images that approaches fidelity to scene observation must combine dynamic range compression, color consistency-a computational analog for human vision color constancy-and color and lightness tonal rendition. In this paper, we extend a previously designed single-scale center/surround retinex to a multiscale version that achieves simultaneous dynamic range compression/color consistency/lightness rendition. This extension fails to produce good color rendition for a class of images that contain violations of the gray-world assumption implicit to the theoretical foundation of the retinex. Therefore, we define a method of color restoration that corrects for this deficiency at the cost of a modest dilution in color consistency. Extensive testing of the multiscale retinex with color restoration on several test scenes and over a hundred images did not reveal any pathological behaviour.

Space missions are increasingly requiring high resolution imagery and, hence, high data compression-two tasks which are inherently incompatible. A tradeoff must, therefore, be made between the available bandwidth and achievable resolution. An efficient visual communication channel-the process from image acquisition to image display-is key to the efficient transmission of the high resolution data. Our algorithm uses wavelet decomposition followed by entropy coding for compression as an integral part of the design of the visual communication channel, where the encoding process constitutes an additional noise source. This allows the development of analysis and synthesis filters which depend not only on the wavelet function, but also on the critical factors which constrain the visual communication channel itself. We feel that this approach is essential in designing a channel which provides the most information, and hence the highest resolution, for the least data.

This paper deals with the extension of information theory to the assessment of visual communication from scene to observer. The mathematical development rigorously unites the electro-optical design of image gathering and display devices with the digital processing algorithms for image coding and restoration. Results show that: end-to-end system analysis closely correlates with measurable and perceptual performance characteristics, such as data rate and image quality, respectively. The goal of producing the best possible image at the lowest data rate can be realized only if (a) the electro-optical design of the image-gathering device is optimized for the maximum-realizable information rate and (b) the image-restoration algorithm properly accounts for the perturbations in the visual communication channel.

The retinex is a human perception-based image processing algorithm which provides color constancy and dynamic range compression. We have previously reported on a single-scale retinex (SSR) and shown that it can either achieve color/lightness rendition or dynamic range compression, but not both simultaneously. We now present a multi-scale retinex (MSR) which overcomes this limitation for most scenes. Both color rendition and dynamic range compression are successfully accomplished except for some "pathological" scenes that have very strong spectral characteristics in a single band.

A novel imaging process consisting of multiresponse imaging and Wiener-matrix restoration is presented. This process acquires and reassembles A multiresponse images into a single minimum mean square restoration error (MSRE) image with a resolution that is square root A times finer than the photodetector-array sampling lattice.>

An approach is presented for subband coding of images. This process not only accounts for the aliasing effects due to non-ideal subband filters, but also takes into account the degradations introduced in the transmission due to channel noise, undersampling of the source, and optical blur introduced by the acquisition device. It is shown that the resultant restored image is far superior to any others formed by either simple reconstruction or restoration.>