Digital image processing heavily relies on the connectivity of pixels, as it is a vital component for accurate object identification and analysis within an image. Grouping together pixels with similar features such colour intensity, allows formation meaningful patterns or objects, which essential recognition segmentation. This approach particularly valuable in photogrammetric imaging, video sur...

In this paper, two local activity-tuned filtering frameworks are proposed for noise removal and image smoothing, where the local activity measurement is given by the clipped and normalized local variance or standard deviation. The first framework is a modified anisotropic diffusion for noise removal of piece-wise smooth image. The second framework is a local activity-tuned Relative Total Variat...

introduction: filtering can greatly affect the quality of clinical images. determining the best filter and the proper degree of smoothing can help to ensure the most accurate diagnosis. methods: forty five patient’s data aquired during brain phantom spect studies were reconstructed using filtered back-projection technique. the ramp, shepp-logan, cosine, hamming, hanning, butterworth, metz and w...

texture image analysis is one of the most important working realms of image processing in medical sciences and industry. up to present, different approaches have been proposed for segmentation of texture images. in this paper, we offered unsupervised texture image segmentation based on markov random field (mrf) model. first, we used gabor filter with different parameters’ (frequency, orientatio...

This paper presents an image blending approach which combines optimal seam finding and transition smoothing for merging a set of aligned source images into a composite panoramic image seamlessly. In this approach, graph cut optimization is used for finding optimal seams in overlapping areas of the source images to create a composite image. If the seams in the composite image are still visible, ...

pixels whereas the source image is denoised while preserving textures in Fig. 11(d). Increasing the level of smoothing in the AT model results in noise-free results as presented in Fig. 11(c), however the textured regions are also smoothed out during the process. Figure 12 illustrates the results of two more texture preserving denoising experiments. Figures 12(c) and (d) are obtained using the ...

Image smoothing is a critical preprocessing step in many image processing tasks. In this paper, a generalized edge-preserving smoothing model is derived from robust statistics theory, and its connections to anisotropic diffusion and bilateral filtering are established. The relationships allow us to derive an improved numerical scheme in the context of a robust estimation process for edge preser...

Structure-preserving image smoothing aims to extract semantically meaningful image structure from texture, which is one of the fundamental problems in computer vision and graphics. However, it is still not clear how to define this concept. On the other hand, semantic image labeling has achieved significant progress recently and has been widely used in many computer vision tasks. In this paper, ...