Histogram equalization

About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.

Digital signal processor with efficient RGB interpolation and histogram accumulation

Abstract: We present a new digital signal processor developed for digital camcorder applications. Taking the digital image signal from A/D converter, the signal processor generates luminance and chrominance signals of the image using an efficient RGB interpolation algorithm and histogram accumulation. We propose a low-cost RGB interpolation algorithm that has little image degradation and show the usefulness of histogram accumulation implemented in the signal processor.

Using Illumination Invariant Color Histogram Descriptors for Recognit ion

Abstract: Color pixel distributions provide a useful cue for object recognition. Recently, for example, a technique called color indexing due to Swain and Ballard [6] used color histograms for the efficient recognition of objects from a large database in the presence of changes in scene geometry and occlusion. The effectiveness of this and other approaches that match color distributions, however, depends on the approximate constancy of the scene illumination. In this paper, we develop color histogram descriptors that are invariant to changes in the intensity and spectral distribution of the illumination. We present a set of experiments that dernonstrate the effectiveness of these descriptors for object recognition in the presence of changes in illuminant spectral power distribution.

Analysis and implementation of Kidney stone detection by reaction diffusion level set segmentation using xilinx system generator on FPGA

Abstract: Ultrasound imaging is one of the available imaging techniques used for diagnosis of kidney abnormalities, which may be like change in shape and position and swelling of limb; there are also other Kidney abnormalities such as formation of stones, cysts, blockage of urine, congenital anomalies, and cancerous cells. During surgical processes it is vital to recognize the true and precise location of kidney stone. The detection of kidney stones using ultrasound imaging is a highly challenging task as they are of low contrast and contain speckle noise. This challenge is overcome by employing suitable image processing techniques. The ultrasound image is first preprocessed to get rid of speckle noise using the image restoration process. The restored image is smoothened using Gabor filter and the subsequent image is enhanced by histogram equalization. The preprocessed image is achieved with level set segmentation to detect the stone region. Segmentation process is employed twice for getting better results; first to segment kidney portion and then to segment the stone portion, respectively. In this work, the level set segmentation uses two terms, namely, momentum and resilient propagation (Rprop) to detect the stone portion. After segmentation, the extracted region of the kidney stone is given to Symlets, Biorthogonal (bio3.7, bio3.9, and bio4.4), and Daubechies lifting scheme wavelet subbands to extract energy levels. These energy levels provide evidence about presence of stone, by comparing them with that of the normal energy levels. They are trained by multilayer perceptron (MLP) and back propagation (BP) ANN to classify and its type of stone with an accuracy of 98.8%. The prosed work is designed and real time is implemented on both Filed Programmable Gate Array Vertex-2Pro FPGA using Xilinx System Generator (XSG) Verilog and Matlab 2012a.

Salp Swarm Algorithm-Based Optimally Weighted Histogram Framework for Image Enhancement

Abstract: This article introduces a novel optimally selected plateau limit (PL)-based histogram modification framework. This approach preserves the brightness and improves the contrast of an image effectively without introducing absurd visual deterioration, unnatural contrast effects, and structural artifacts. It also enhances the weak illumination situations, such as backlighting effect and the nonuniform illumination of images without introducing any undesirable artifacts. The proposed method based on the subhistogram and clipping operations utilizes the PLs to modify the histogram of the image before applying the histogram equalization approach. The salp swarm algorithm (SSA)-based optimization technique is incorporated to compute the optimal PLs or adaptive weighted limits. To prove the efficiency of the proposed algorithm, a comparative study is done with the well-known histogram-based processing techniques and state-of-art methods in the literature. Furthermore, well-recognized different evaluation parameters are considered to compare the proposed framework with other existing methods.