Abstract. Segmenting noisy and intensity-inhomogeneous images presents a significant challenge in image segmentation. This paper proposes a novel additive-bias-correction (ABC) variational segmentation model combined with an efficient iterative convolution-thresholding (ICT) solver, termed the ABC-ICT method, to address this issue. The input image is assumed to be additively decomposed into three components: a homogeneous structure, a bias field characterizing the intensity inhomogeneity, and imaging noise. Based on this additive decomposition assumption, our variational minimization model, implemented using the ICT method, consists of four energy parts: total variation denoising, local image smoothing, local bias-corrected segmentation, and contour length regularization, enhancing its robustness to noise and intensity inhomogeneity. Due to the use of characteristic functions, the proposed ABC-ICT method typically converges faster than the commonly used level set approach, naturally handles topological changes, and facilitates multiphase segmentation. Additionally, it offers several advantages, including simultaneous image segmentation, intensity inhomogeneity correction, and noise removal. Moreover, the total energy decays with each iteration, ensuring that the iterative scheme always converges to a minimum. We validate the unconditionally energy-decaying property both theoretically and experimentally. Numerical experiments and comparisons with existing models demonstrate the effectiveness and efficiency of the proposed model.

Contrast enhancement plays an important role in image/video processing and computer vision applications. Its main purpose is to adjust the image intensity to enhance the quality and features of the image. In this paper, we propose a simple and efficient adaptive variational model for contrast enhancement for partially shaded low-light images. The key idea of this adaptive approach is to employ the maximum image of the RGB color channels as a classifier to divide the image domain into the relatively bright and dim parts, and then use different fitting terms for each part such that the bright pixels are preserved as close as possible to the original ones while the dim pixels are boosted with brightness and contrast-level parameters to adjust the degree of the strength. With this adaptivity, one can find that the proposed model considerably improves upon the existing variational models in the literature. In this paper, the existence and uniqueness of the minimizer for the variational minimization problem is established. The split Bregman method is used to accomplish an efficient numerical implementation of the adaptive variational model. Moreover, a number of numerical experiments and comparisons with other popular enhancement methods are conducted to demonstrate the high performance of the newly proposed method.

This paper presents a very efficient VLSI architecture for real-time median filtering as requested in many image/video applications. The median is obtained by first sorting input sequences and then selecting identified order according to the number of inputs. To reach the goal of high-speed data sorting, an optimized delete-and-insert algorithm is derived and then mapped onto shiftable content-addressable memory architecture. The complete design can be decomposed into a set of processor elements, where each processor element consists of two basic cells-sort-cell and compare-cell. Thus the design becomes very regular. More specifically any specified order can be obtained within one cycle and a high-speed clock rate can be achieved. A prototype chip for 64 samples based on this architecture has been implemented and tested. Results show that a clock rate up to 50 MHz can be achieved using a 1.2 /spl mu/m CMOS double metal technology.>