Edges in a digital image provide important information about the objects contained within the image since they constitute boundaries between objects in the image. This paper proposes a new approach based on independent component analysis (ICA) for edge-detection in noisy images. The proposed approach works in two phases—the training phase and the edge-detection phase. The training phase is carried out only once to determine parameters for the ICA. Once calculated, these ICA parameters can be employed for edge-detection in any number of noisy images. The edge-detection phase deals with transitioning in and out of ICA domain and recovering the original image from a noisy image. Both gray scale as well as colored images corrupted with Gaussian noise are studied using the proposed approach, and remarkably improved results, compared to the existing edge-detection techniques, are achieved. Performance evaluation of the proposed approach using both subjective as well as objective methods is presented. 1. Introduction In typical images, edges characterize the object boundaries with sharp changes in intensity levels. Edges are useful for segmentation, registration, and identification of objects in a given scene. Most of these operations have edge-detection as the preprocessing step. Consequently, the success of these image processing tasks depends strictly on the performance of edge-detection step. Detection of these edges, therefore, not only helps in more accurate representation of an image, but it also significantly simplifies its processing. The edge detection usually involves calculation of derivative of the image intensity function at a given pixel location owing to the fact that image intensity shows sudden changes at edges. Pixels with relatively higher magnitude of derivative of the image intensity function are classified into edge pixels. To this end, Gradient and Laplacian operators/masks such as Prewitt, Roberts, Sobel, Canny [1–3] are usually employed for the purpose of edge-detection. These operators work well for specific cases; however, they fail for others. For instance, the Prewitt edge detector works quite well for digital images corrupted with Poisson noise, whereas its performance decreases sharply for other kinds of noise [4]. Moreover, these operators/masks are chosen independent of the image under consideration. As such, the performance of these operators masks degrades considerably with the increase in noise levels in images [2]. Unfortunately, digital images are inevitably degraded by noise during acquisition and/or transmission.
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