Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms

Abstract

Digital image contrast enhancement methods that are based on histogram equalization technique are still useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Therefore, this paper proposes a new local image contrast enhancement method, based on histogram equalization technique, which not only enhances the contrast, but also increases the sharpness of the image. Besides, this method is also able to preserve the mean brightness of the image. In order to limit the noise amplification, this newly proposed method utilizes local mean-separation, and clipped histogram bins methodologies. Based on nine test color images and the benchmark with other three histogram equalization based methods, the proposed technique shows the best overall performance.

Transferable Architecture for Segmenting Maxillary Sinuses on Texture-Enhanced Occipitomental View Radiographs

“…Based on CD scores in Table 3, both prior methods are lower in CD scores compared with the proposed technique. These findings may confer that both prior methods [7,8] are still able to improve the image contrast but with lower improvement index than the ToMA offers. In addition, Table 3 shows the average index of CE for both prior methods with around 14.5 and 7 folds higher than the CE index of ToMA, respectively.…”

“…The final feature maps from f c7 are further processed using bilinear interpolation technique on the score_ f r layer, which generates class presence maps. To obtain the final prediction map (P d ), a deconvolutional layer (deconv) performs a convolutional counterpart, given the definition of the f k,c () in Equation (8). The details of these layers are also available in Table 1.…”

“…The proposed ToMA algorithm along with the prior state-of-the-arts [7,8] were implemented using the hardware of an Intel Core i7-5500U @ 2.40 GHz, 4 GB of RAM, and 1TB HDD at 5400 RPM with a 64-bit Windows 10 Home Version operating system.…”

“…To benchmark the performance of the proposed method, two prior state-of-the-arts in the contrast enhancement methods [7,8] were tested on the OSXR dataset. Based on CD scores in Table 3, both prior methods are lower in CD scores compared with the proposed technique.…”

“…Few studies [7][8][9][10][11] have developed histogram-based image enhancement for medical images that remaps the pixel distributions to have higher contrast ratio. Although final contrast ratio might improve, there are few drawbacks, which have decimated the merits of prior arts, including noise amplification [7], generation of undesired texture [8], and inferior improvement on contrast ratio over the soft tissues [7,8]. These issues are aimed to be addressed through the first part of the proposed framework, image enhancement.…”